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ORCIDMax Welling
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- affiliation: University of Amsterdam, Informatics Institute, The Netherlands
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2020 – today
- 2025
[i188]Dario Coscia, Max Welling, Nicola Demo, Gianluigi Rozza:
BARNN: A Bayesian Autoregressive and Recurrent Neural Network. CoRR abs/2501.18665 (2025)- [i187]Maksim Zhdanov, Max Welling, Jan-Willem van de Meent:
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems. CoRR abs/2502.17019 (2025) - 2024
- [j37]Ilia Igashov
, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia:
Equivariant 3D-conditional diffusion model for molecular linker design. Nat. Mac. Intell. 6(4): 417-427 (2024) - [j36]Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Rokkum Jamasb, Ilia Igashov, Weitao Du, Carla P. Gomes, Tom L. Blundell, Pietro Lio, Max Welling, Michael M. Bronstein, Bruno E. Correia:
Structure-based drug design with equivariant diffusion models. Nat. Comput. Sci. 4(12): 899-909 (2024) - [c207]Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling:
Protect Your Score: Contact-Tracing with Differential Privacy Guarantees. AAAI 2024: 14829-14837 - [c206]T. Anderson Keller, Lyle Muller, Terrence J. Sejnowski, Max Welling:
Traveling Waves Encode The Recent Past and Enhance Sequence Learning. ICLR 2024 - [c205]Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger:
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers. ICLR 2024 - [c204]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [c203]Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth, Max Welling, Jan-Willem van de Meent:
Variational Flow Matching for Graph Generation. NeurIPS 2024 - [i186]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i185]Sindy Löwe, Francesco Locatello, Max Welling:
Binding Dynamics in Rotating Features. CoRR abs/2402.05627 (2024) - [i184]Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling:
DNA: Differentially private Neural Augmentation for contact tracing. CoRR abs/2404.13381 (2024) - [i183]Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Thambiratnam, Alex Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris:
Aurora: A Foundation Model of the Atmosphere. CoRR abs/2405.13063 (2024) - [i182]Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth, Max Welling, Jan-Willem van de Meent:
Variational Flow Matching for Graph Generation. CoRR abs/2406.04843 (2024) - [i181]T. Anderson Keller, Lyle Muller, Terrence J. Sejnowski, Max Welling:
A Spacetime Perspective on Dynamical Computation in Neural Information Processing Systems. CoRR abs/2409.13669 (2024) - [i180]Mathis Gerdes, Max Welling, Miranda C. N. Cheng:
GUD: Generation with Unified Diffusion. CoRR abs/2410.02667 (2024) - [i179]Yue Song, T. Anderson Keller, Yisong Yue, Pietro Perona, Max Welling:
Unsupervised Representation Learning from Sparse Transformation Analysis. CoRR abs/2410.05564 (2024) - [i178]Takeru Miyato, Sindy Löwe, Andreas Geiger, Max Welling:
Artificial Kuramoto Oscillatory Neurons. CoRR abs/2410.13821 (2024) - 2023
- [j35]Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik:
Scientific discovery in the age of artificial intelligence. Nat. 620(7972): 47-60 (2023) - [c202]Rob Romijnders, Yuki M. Asano, Christos Louizos, Max Welling:
No time to waste: practical statistical contact tracing with few low-bit messages. AISTATS 2023: 7943-7960 - [c201]Winfried van den Dool, Tijmen Blankevoort, Max Welling, Yuki M. Asano:
Efficient Neural PDE-Solvers using Quantization Aware Training. ICCV (Workshops) 2023: 1415-1424 - [c200]Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta:
Clifford Neural Layers for PDE Modeling. ICLR 2023 - [c199]T. Anderson Keller, Max Welling:
Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks. ICML 2023: 16168-16189 - [c198]David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter:
Geometric Clifford Algebra Networks. ICML 2023: 29306-29337 - [c197]Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling:
Latent Traversals in Generative Models as Potential Flows. ICML 2023: 32288-32303 - [c196]Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh:
Lie Point Symmetry and Physics-Informed Networks. NeurIPS 2023 - [c195]Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling:
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths. NeurIPS 2023 - [c194]Sindy Löwe, Phillip Lippe, Francesco Locatello, Max Welling:
Rotating Features for Object Discovery. NeurIPS 2023 - [c193]Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani:
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. NeurIPS 2023 - [c192]Yue Song, Andy Keller, Nicu Sebe, Max Welling:
Flow Factorized Representation Learning. NeurIPS 2023 - [c191]Tim Bakker, Herke van Hoof, Max Welling:
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. ECML/PKDD (1) 2023: 3-19 - [i177]Alexandre Adam, Laurence Perreault Levasseur, Yashar Hezaveh, Max Welling:
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines. CoRR abs/2301.04168 (2023) - [i176]David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter:
Geometric Clifford Algebra Networks. CoRR abs/2302.06594 (2023) - [i175]Evgenii Egorov, Roberto Bondesan, Max Welling:
The END: An Equivariant Neural Decoder for Quantum Error Correction. CoRR abs/2304.07362 (2023) - [i174]Yue Song, Andy Keller, Nicu Sebe, Max Welling:
Latent Traversals in Generative Models as Potential Flows. CoRR abs/2304.12944 (2023) - [i173]Sindy Löwe, Phillip Lippe, Francesco Locatello, Max Welling:
Rotating Features for Object Discovery. CoRR abs/2306.00600 (2023) - [i172]Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani:
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. CoRR abs/2307.07050 (2023) - [i171]Winfried van den Dool, Tijmen Blankevoort, Max Welling, Yuki M. Asano:
Efficient Neural PDE-Solvers using Quantization Aware Training. CoRR abs/2308.07350 (2023) - [i170]Tim Bakker, Herke van Hoof, Max Welling:
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. CoRR abs/2309.05477 (2023) - [i169]T. Anderson Keller, Lyle Muller, Terrence J. Sejnowski, Max Welling:
Traveling Waves Encode the Recent Past and Enhance Sequence Learning. CoRR abs/2309.08045 (2023) - [i168]Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling:
Flow Factorized Representation Learning. CoRR abs/2309.13167 (2023) - [i167]Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger:
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers. CoRR abs/2310.10375 (2023) - [i166]Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh:
Lie Point Symmetry and Physics Informed Networks. CoRR abs/2311.04293 (2023) - [i165]Luisa H. B. Liboni, Roberto C. Budzinski, Alexandra N. Busch, Sindy Löwe, Thomas A. Keller, Max Welling, Lyle E. Muller:
Image segmentation with traveling waves in an exactly solvable recurrent neural network. CoRR abs/2311.16943 (2023) - [i164]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (2023) - [i163]Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling:
Protect Your Score: Contact Tracing With Differential Privacy Guarantees. CoRR abs/2312.11581 (2023) - 2022
- [j34]Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling:
Complex-Valued Autoencoders for Object Discovery. Trans. Mach. Learn. Res. 2022 (2022) - [c190]Zhuo Su, Max Welling, Matti Pietikäinen, Li Liu:
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation. 3DV 2022: 547-556 - [c189]Kirill Neklyudov, Max Welling:
Orbital MCMC. AISTATS 2022: 5790-5814 - [c188]Sindy Löwe, David Madras, Richard S. Zemel, Max Welling:
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CLeaR 2022: 509-525 - [c187]Wouter Kool, Herke van Hoof, Joaquim A. S. Gromicho, Max Welling:
Deep Policy Dynamic Programming for Vehicle Routing Problems. CPAIOR 2022: 190-213 - [c186]Shreya Kadambi, Arash Behboodi, Joseph B. Soriaga, Max Welling, Roohollah Amiri, Srinivas Yerramalli, Taesang Yoo:
Neural RF SLAM for unsupervised positioning and mapping with channel state information. ICC 2022: 3238-3244 - [c185]Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J. Bekkers, Max Welling:
Geometric and Physical Quantities improve E(3) Equivariant Message Passing. ICLR 2022 - [c184]Johannes Brandstetter, Daniel E. Worrall, Max Welling:
Message Passing Neural PDE Solvers. ICLR 2022 - [c183]Elise van der Pol, Herke van Hoof, Frans A. Oliehoek, Max Welling:
Multi-Agent MDP Homomorphic Networks. ICLR 2022 - [c182]Johannes Brandstetter, Max Welling, Daniel E. Worrall:
Lie Point Symmetry Data Augmentation for Neural PDE Solvers. ICML 2022: 2241-2256 - [c181]Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling:
Equivariant Diffusion for Molecule Generation in 3D. ICML 2022: 8867-8887 - [c180]Gabriele Cesa, Arash Behboodi, Taco S. Cohen, Max Welling:
On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane. NeurIPS 2022 - [c179]Anna Kuzina, Max Welling, Jakub M. Tomczak:
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC. NeurIPS 2022 - [c178]ChangYong Oh, Roberto Bondesan, Efstratios Gavves, Max Welling:
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel. NeurIPS 2022 - [i162]Johannes Brandstetter, Daniel E. Worrall, Max Welling:
Message Passing Neural PDE Solvers. CoRR abs/2202.03376 (2022) - [i161]Johannes Brandstetter, Max Welling, Daniel E. Worrall:
Lie Point Symmetry Data Augmentation for Neural PDE Solvers. CoRR abs/2202.07643 (2022) - [i160]Shreya Kadambi, Arash Behboodi, Joseph B. Soriaga, Max Welling, Roohollah Amiri, Srinivas Yerramalli, Taesang Yoo:
Neural RF SLAM for unsupervised positioning and mapping with channel state information. CoRR abs/2203.08264 (2022) - [i159]Anna Kuzina, Max Welling, Jakub M. Tomczak:
Defending Variational Autoencoders from Adversarial Attacks with MCMC. CoRR abs/2203.09940 (2022) - [i158]Shi Hu, Eric T. Nalisnick, Max Welling:
Adversarial Defense via Image Denoising with Chaotic Encryption. CoRR abs/2203.10290 (2022) - [i157]Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling:
Equivariant Diffusion for Molecule Generation in 3D. CoRR abs/2203.17003 (2022) - [i156]Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling:
Complex-Valued Autoencoders for Object Discovery. CoRR abs/2204.02075 (2022) - [i155]Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling:
Path Integral Stochastic Optimal Control for Sampling Transition Paths. CoRR abs/2207.02149 (2022) - [i154]ChangYong Oh, Roberto Bondesan, Dana Kianfar, Rehan Ahmed, Rishubh Khurana, Payal Agarwal, Romain Lepert, Mysore Sriram, Max Welling:
Bayesian Optimization for Macro Placement. CoRR abs/2207.08398 (2022) - [i153]Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta:
Clifford Neural Layers for PDE Modeling. CoRR abs/2209.04934 (2022) - [i152]Zhuo Su, Max Welling, Matti Pietikäinen, Li Liu:
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation. CoRR abs/2209.05924 (2022) - [i151]Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia:
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design. CoRR abs/2210.05274 (2022) - [i150]Arne Schneuing, Yuanqi Du, Charles Harris, Arian R. Jamasb, Ilia Igashov, Weitao Du, Tom L. Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael M. Bronstein, Bruno E. Correia:
Structure-based Drug Design with Equivariant Diffusion Models. CoRR abs/2210.13695 (2022) - [i149]Priyank Jaini, Kristian Kersting, Antonio Vergari, Max Welling:
Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161). Dagstuhl Reports 12(4): 13-25 (2022) - [i148]David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling:
Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). Dagstuhl Reports 12(8): 20-30 (2022) - 2021
- [j33]Kumar Pratik, Bhaskar D. Rao, Max Welling:
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection. IEEE Trans. Signal Process. 69: 459-473 (2021) - [c177]Victor Garcia Satorras, Max Welling:
Neural Enhanced Belief Propagation on Factor Graphs. AISTATS 2021: 685-693 - [c176]Priyank Jaini, Didrik Nielsen, Max Welling:
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. AISTATS 2021: 3349-3357 - [c175]Kumar Pratik, Rana Ali Amjad, Arash Behboodi, Joseph B. Soriaga, Max Welling:
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking. GLOBECOM 2021: 1-6 - [c174]T. Anderson Keller, Max Welling:
Predictive Coding with Topographic Variational Autoencoders. ICCVW 2021: 1086-1091 - [c173]Marc Anton Finzi, Roberto Bondesan, Max Welling:
Probabilistic Numeric Convolutional Neural Networks. ICLR 2021 - [c172]Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling:
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. ICLR 2021 - [c171]Roberto Bondesan, Max Welling:
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning. ICML 2021: 1038-1048 - [c170]Marc Finzi, Max Welling, Andrew Gordon Wilson:
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. ICML 2021: 3318-3328 - [c169]Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling:
Federated Learning of User Verification Models Without Sharing Embeddings. ICML 2021: 4328-4336 - [c168]T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling:
Self Normalizing Flows. ICML 2021: 5378-5387 - [c167]Victor Garcia Satorras, Emiel Hoogeboom, Max Welling:
E(n) Equivariant Graph Neural Networks. ICML 2021: 9323-9332 - [c166]Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Daniel Rueckert, Jingu Lee, Nicola Pezzotti, Elwin de Weerdt, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Franciscus Van Gemert, Christophe Schülke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. van Osch, Marius Staring, Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun, Hyungseob Shin, Yohan Jun, Taejoon Eo, Sewon Kim, Taeseong Kim, Dosik Hwang, Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan W. A. Caan, Max Welling, Matthew J. Muckley, Florian Knoll:
Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. MLMIR@MICCAI 2021: 25-34 - [c165]Shi Hu, Nicola Pezzotti, Max Welling:
Learning to Predict Error for MRI Reconstruction. MICCAI (3) 2021: 604-613 - [c164]Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, Max Welling:
E(n) Equivariant Normalizing Flows. NeurIPS 2021: 4181-4192 - [c163]Farhad Ghazvinian Zanjani, Ilia Karmanov, Hanno Ackermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli:
Modality-Agnostic Topology Aware Localization. NeurIPS 2021: 10457-10468 - [c162]Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling:
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. NeurIPS 2021: 12454-12465 - [c161]Priyank Jaini, Lars Holdijk, Max Welling:
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent. NeurIPS 2021: 16727-16737 - [c160]T. Anderson Keller, Max Welling:
Topographic VAEs learn Equivariant Capsules. NeurIPS 2021: 28585-28597 - [c159]Shi Hu, Egill A. Fridgeirsson, Guido van Wingen, Max Welling:
Transformer-Based Deep Survival Analysis. SPACA 2021: 132-148 - [c158]ChangYong Oh, Efstratios Gavves, Max Welling:
Mixed variable Bayesian optimization with frequency modulated kernels. UAI 2021: 950-960 - [i147]Priyank Jaini, Didrik Nielsen, Max Welling:
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. CoRR abs/2102.02374 (2021) - [i146]Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling:
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. CoRR abs/2102.05379 (2021) - [i145]Victor Garcia Satorras, Emiel Hoogeboom, Max Welling:
E(n) Equivariant Graph Neural Networks. CoRR abs/2102.09844 (2021) - [i144]Wouter Kool, Herke van Hoof, Joaquim A. S. Gromicho, Max Welling:
Deep Policy Dynamic Programming for Vehicle Routing Problems. CoRR abs/2102.11756 (2021) - [i143]ChangYong Oh, Efstratios Gavves, Max Welling:
Mixed Variable Bayesian Optimization with Frequency Modulated Kernels. CoRR abs/2102.12792 (2021) - [i142]ChangYong Oh, Roberto Bondesan, Efstratios Gavves, Max Welling:
Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels. CoRR abs/2102.13382 (2021) - [i141]Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij:
Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions. CoRR abs/2103.04786 (2021) - [i140]Roberto Bondesan, Max Welling:
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning. CoRR abs/2103.04913 (2021) - [i139]Anna Kuzina, Max Welling, Jakub M. Tomczak:
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks. CoRR abs/2103.06701 (2021) - [i138]Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling:
Federated Learning of User Verification Models Without Sharing Embeddings. CoRR abs/2104.08776 (2021) - [i137]Marc Finzi, Max Welling, Andrew Gordon Wilson:
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. CoRR abs/2104.09459 (2021) - [i136]Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, Max Welling:
E(n) Equivariant Normalizing Flows for Molecule Generation in 3D. CoRR abs/2105.09016 (2021) - [i135]Maurice Weiler, Patrick Forré, Erik Verlinde, Max Welling:
Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. CoRR abs/2106.06020 (2021) - [i134]Priyank Jaini, Lars Holdijk, Max Welling:
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent. CoRR abs/2106.07832 (2021) - [i133]Kirill Neklyudov, Roberto Bondesan, Max Welling:
Deterministic Gibbs Sampling via Ordinary Differential Equations. CoRR abs/2106.10188 (2021) - [i132]Matthias Reisser, Christos Louizos, Efstratios Gavves, Max Welling:
Federated Mixture of Experts. CoRR abs/2107.06724 (2021) - [i131]T. Anderson Keller, Max Welling:
Topographic VAEs learn Equivariant Capsules. CoRR abs/2109.01394 (2021) - [i130]Kumar Pratik, Rana Ali Amjad, Arash Behboodi, Joseph B. Soriaga, Max Welling:
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking. CoRR abs/2109.12561 (2021) - [i129]Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J. Bekkers, Max Welling:
Geometric and Physical Quantities improve E(3) Equivariant Message Passing. CoRR abs/2110.02905 (2021) - [i128]Elise van der Pol, Herke van Hoof, Frans A. Oliehoek, Max Welling:
Multi-Agent MDP Homomorphic Networks. CoRR abs/2110.04495 (2021) - [i127]T. Anderson Keller, Qinghe Gao, Max Welling:
Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders. CoRR abs/2110.13911 (2021) - [i126]Christos Louizos, Matthias Reisser, Joseph Soriaga, Max Welling:
An Expectation-Maximization Perspective on Federated Learning. CoRR abs/2111.10192 (2021) - [i125]Kirill Neklyudov, Priyank Jaini, Max Welling:
Particle Dynamics for Learning EBMs. CoRR abs/2111.13772 (2021) - 2020
- [j32]Zeynep Akata, Dan Balliet, Maarten de Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens, Davide Grossi, Koen V. Hindriks, Holger H. Hoos, Hayley Hung, Catholijn M. Jonker, Christof Monz, Mark A. Neerincx, Frans A. Oliehoek, Henry Prakken, Stefan Schlobach, Linda C. van der Gaag, Frank van Harmelen, Herke van Hoof, Birna van Riemsdijk, Aimee van Wynsberghe, Rineke Verbrugge, Bart Verheij, Piek Vossen, Max Welling:
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer 53(8): 18-28 (2020) - [j31]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Variational Bayes In Private Settings (VIPS). J. Artif. Intell. Res. 68: 109-157 (2020) - [j30]Wouter Kool, Herke van Hoof, Max Welling:
Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement. J. Mach. Learn. Res. 21: 47:1-47:36 (2020) - [j29]Raghavendra Selvan, Thomas Kipf, Max Welling, Antonio Garcia-Uceda Juarez, Jesper Holst Pedersen, Jens Petersen, Marleen de Bruijne:
Graph refinement based airway extraction using mean-field networks and graph neural networks. Medical Image Anal. 64: 101751 (2020) - [c157]Elise van der Pol, Thomas Kipf, Frans A. Oliehoek, Max Welling:
Plannable Approximations to MDP Homomorphisms: Equivariance under Actions. AAMAS 2020: 1431-1439 - [c156]Xiahan Shi, Leonard Salewski, Martin Schiegg, Max Welling:
Relational Generalized Few-Shot Learning. BMVC 2020 - [c155]Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, Zhuowen Tu:
Guided Variational Autoencoder for Disentanglement Learning. CVPR 2020: 7917-7926 - [c154]Wouter Kool, Herke van Hoof, Max Welling:
Estimating Gradients for Discrete Random Variables by Sampling without Replacement. ICLR 2020 - [c153]Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling:
Gradient $\ell_1$ Regularization for Quantization Robustness. ICLR 2020 - [c152]Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling:
Batch-shaping for learning conditional channel gated networks. ICLR 2020 - [c151]Thomas N. Kipf, Elise van der Pol, Max Welling:
Contrastive Learning of Structured World Models. ICLR 2020 - [c150]Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang:
To Relieve Your Headache of Training an MRF, Take AdVIL. ICLR 2020 - [c149]Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov:
Involutive MCMC: a Unifying Framework. ICML 2020: 7273-7282 - [c148]Max Welling:
Integrating Generative Modeling into Deep Learning. ICPRAM 2020: 9 - [c147]James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling:
Variational Bayes in Private Settings (VIPS) (Extended Abstract). IJCAI 2020: 5050-5054 - [c146]Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling:
DIVA: Domain Invariant Variational Autoencoders. MIDL 2020: 322-348 - [c145]Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling:
Bayesian Bits: Unifying Quantization and Pruning. NeurIPS 2020 - [c144]Tim Bakker, Herke van Hoof, Max Welling:
Experimental design for MRI by greedy policy search. NeurIPS 2020 - [c143]Fabian Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling:
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. NeurIPS 2020 - [c142]Pim de Haan, Taco S. Cohen, Max Welling:
Natural Graph Networks. NeurIPS 2020 - [c141]Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling:
The Convolution Exponential and Generalized Sylvester Flows. NeurIPS 2020 - [c140]Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling:
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. NeurIPS 2020 - [c139]Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. NeurIPS 2020 - [i124]Shi Hu, Nicola Pezzotti, Dimitrios Mavroeidis, Max Welling:
Simple and Accurate Uncertainty Quantification from Bias-Variance Decomposition. CoRR abs/2002.05582 (2020) - [i123]Wouter Kool, Herke van Hoof, Max Welling:
Estimating Gradients for Discrete Random Variables by Sampling without Replacement. CoRR abs/2002.06043 (2020) - [i122]Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling:
Gradient 𝓁1 Regularization for Quantization Robustness. CoRR abs/2002.07520 (2020) - [i121]Elise van der Pol, Thomas N. Kipf, Frans A. Oliehoek, Max Welling:
Plannable Approximations to MDP Homomorphisms: Equivariance under Actions. CoRR abs/2002.11963 (2020) - [i120]Victor Garcia Satorras, Max Welling:
Neural Enhanced Belief Propagation on Factor Graphs. CoRR abs/2003.01998 (2020) - [i119]Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling:
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. CoRR abs/2003.05425 (2020) - [i118]Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, Zhuowen Tu:
Guided Variational Autoencoder for Disentanglement Learning. CoRR abs/2004.01255 (2020) - [i117]Mirgahney Mohamed, Gabriele Cesa, Taco S. Cohen, Max Welling:
A Data and Compute Efficient Design for Limited-Resources Deep Learning. CoRR abs/2004.09691 (2020) - [i116]Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling:
Bayesian Bits: Unifying Quantization and Pruning. CoRR abs/2005.07093 (2020) - [i115]Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling:
The Convolution Exponential and Generalized Sylvester Flows. CoRR abs/2006.01910 (2020) - [i114]Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling:
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. CoRR abs/2006.10503 (2020) - [i113]Sindy Löwe, David Madras, Richard S. Zemel, Max Welling:
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CoRR abs/2006.10833 (2020) - [i112]Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov:
Involutive MCMC: a Unifying Framework. CoRR abs/2006.16653 (2020) - [i111]Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. CoRR abs/2006.16908 (2020) - [i110]Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling:
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. CoRR abs/2007.02731 (2020) - [i109]Hossein Hosseini, Sungrack Yun, Hyunsin Park, Christos Louizos, Joseph Soriaga, Max Welling:
Federated Learning of User Authentication Models. CoRR abs/2007.04618 (2020) - [i108]Pim de Haan, Taco Cohen, Max Welling:
Natural Graph Networks. CoRR abs/2007.08349 (2020) - [i107]Kirill Neklyudov, Max Welling:
Orbital MCMC. CoRR abs/2010.08047 (2020) - [i106]Marc Finzi, Roberto Bondesan, Max Welling:
Probabilistic Numeric Convolutional Neural Networks. CoRR abs/2010.10876 (2020) - [i105]Roberto Bondesan, Max Welling:
Quantum Deformed Neural Networks. CoRR abs/2010.11189 (2020) - [i104]Tim Bakker, Herke van Hoof, Max Welling:
Experimental design for MRI by greedy policy search. CoRR abs/2010.16262 (2020) - [i103]T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling:
Self Normalizing Flows. CoRR abs/2011.07248 (2020)
2010 – 2019
- 2019
- [j28]Diederik P. Kingma, Max Welling:
An Introduction to Variational Autoencoders. Found. Trends Mach. Learn. 12(4): 307-392 (2019) - [j27]Kai Lønning, Patrick Putzky, Jan-Jakob Sonke, Liesbeth Reneman, Matthan W. A. Caan, Max Welling:
Recurrent inference machines for reconstructing heterogeneous MRI data. Medical Image Anal. 53: 64-78 (2019) - [c138]Peter O'Connor, Efstratios Gavves, Max Welling:
Training a Spiking Neural Network with Equilibrium Propagation. AISTATS 2019: 1516-1523 - [c137]Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling:
Data-Free Quantization Through Weight Equalization and Bias Correction. ICCV 2019: 1325-1334 - [c136]Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling:
The Deep Weight Prior. ICLR (Poster) 2019 - [c135]Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling:
DIVA: Domain Invariant Variational Autoencoder. DGS@ICLR 2019 - [c134]Wouter Kool, Herke van Hoof, Max Welling:
Attention, Learn to Solve Routing Problems! ICLR (Poster) 2019 - [c133]Wouter Kool, Herke van Hoof, Max Welling:
Buy 4 REINFORCE Samples, Get a Baseline for Free! DeepRLStructPred@ICLR 2019 - [c132]Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, Max Welling:
Relaxed Quantization for Discretized Neural Networks. ICLR (Poster) 2019 - [c131]Peter O'Connor, Efstratios Gavves, Max Welling:
Initialized Equilibrium Propagation for Backprop-Free Training. ICLR (Poster) 2019 - [c130]Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling:
Gauge Equivariant Convolutional Networks and the Icosahedral CNN. ICML 2019: 1321-1330 - [c129]Emiel Hoogeboom, Rianne van den Berg, Max Welling:
Emerging Convolutions for Generative Normalizing Flows. ICML 2019: 2771-2780 - [c128]Wouter Kool, Herke van Hoof, Max Welling:
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. ICML 2019: 3499-3508 - [c127]Shi Hu, Daniel E. Worrall, Stefan Knegt, Bas Veeling, Henkjan J. Huisman, Max Welling:
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. MICCAI (2) 2019: 137-145 - [c126]Patrick Putzky, Max Welling:
Invert to Learn to Invert. NeurIPS 2019: 444-454 - [c125]ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling:
Combinatorial Bayesian Optimization using the Graph Cartesian Product. NeurIPS 2019: 2910-2920 - [c124]Daniel E. Worrall, Max Welling:
Deep Scale-spaces: Equivariance Over Scale. NeurIPS 2019: 7364-7376 - [c123]Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling:
The Functional Neural Process. NeurIPS 2019: 8743-8754 - [c122]Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg, Max Welling:
Integer Discrete Flows and Lossless Compression. NeurIPS 2019: 12134-12144 - [c121]Victor Garcia Satorras, Max Welling, Zeynep Akata:
Combining Generative and Discriminative Models for Hybrid Inference. NeurIPS 2019: 13802-13812 - [c120]Wenling Shang, Douwe van der Wal, Herke van Hoof, Max Welling:
Stochastic Activation Actor Critic Methods. ECML/PKDD (3) 2019: 103-117 - [c119]Karen Ullrich, Rianne van den Berg, Marcus A. Brubaker, David J. Fleet, Max Welling:
Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem. UAI 2019: 399-411 - [c118]Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. UAI 2019: 733-743 - [i102]Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang:
Adversarial Variational Inference and Learning in Markov Random Fields. CoRR abs/1901.08400 (2019) - [i101]Emiel Hoogeboom, Rianne van den Berg, Max Welling:
Emerging Convolutions for Generative Normalizing Flows. CoRR abs/1901.11137 (2019) - [i100]ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling:
Combinatorial Bayesian Optimization using Graph Representations. CoRR abs/1902.00448 (2019) - [i99]Taco S. Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling:
Gauge Equivariant Convolutional Networks and the Icosahedral CNN. CoRR abs/1902.04615 (2019) - [i98]Wouter Kool, Herke van Hoof, Max Welling:
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. CoRR abs/1903.06059 (2019) - [i97]Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg, Max Welling:
Integer Discrete Flows and Lossless Compression. CoRR abs/1905.07376 (2019) - [i96]Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling:
DIVA: Domain Invariant Variational Autoencoders. CoRR abs/1905.10427 (2019) - [i95]Daniel E. Worrall, Max Welling:
Deep Scale-spaces: Equivariance Over Scale. CoRR abs/1905.11697 (2019) - [i94]Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler, Pim de Haan, Taco S. Cohen, Max Welling:
Covariance in Physics and Convolutional Neural Networks. CoRR abs/1906.02481 (2019) - [i93]Victor Garcia Satorras, Zeynep Akata, Max Welling:
Combining Generative and Discriminative Models for Hybrid Inference. CoRR abs/1906.02547 (2019) - [i92]Diederik P. Kingma, Max Welling:
An Introduction to Variational Autoencoders. CoRR abs/1906.02691 (2019) - [i91]Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling:
Data-Free Quantization through Weight Equalization and Bias Correction. CoRR abs/1906.04721 (2019) - [i90]Karen Ullrich, Rianne van den Berg, Marcus A. Brubaker, David J. Fleet, Max Welling:
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem. CoRR abs/1906.07582 (2019) - [i89]Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling:
The Functional Neural Process. CoRR abs/1906.08324 (2019) - [i88]Shi Hu, Daniel E. Worrall, Stefan Knegt, Bastiaan S. Veeling, Henkjan J. Huisman, Max Welling:
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. CoRR abs/1907.01949 (2019) - [i87]Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling:
Batch-Shaped Channel Gated Networks. CoRR abs/1907.06627 (2019) - [i86]Xiahan Shi, Leonard Salewski, Martin Schiegg, Zeynep Akata, Max Welling:
Relational Generalized Few-Shot Learning. CoRR abs/1907.09557 (2019) - [i85]Frederik Harder, Jonas Köhler, Max Welling, Mijung Park:
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning. CoRR abs/1910.06924 (2019) - [i84]Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan W. A. Caan, Max Welling:
i-RIM applied to the fastMRI challenge. CoRR abs/1910.08952 (2019) - [i83]Patrick Putzky, Max Welling:
Invert to Learn to Invert. CoRR abs/1911.10914 (2019) - [i82]Thomas N. Kipf, Elise van der Pol, Max Welling:
Contrastive Learning of Structured World Models. CoRR abs/1911.12247 (2019) - [i81]Christina Winkler, Daniel E. Worrall, Emiel Hoogeboom, Max Welling:
Learning Likelihoods with Conditional Normalizing Flows. CoRR abs/1912.00042 (2019) - [i80]Andrey Kuzmin, Markus Nagel, Saurabh Pitre, Sandeep Pendyam, Tijmen Blankevoort, Max Welling:
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks. CoRR abs/1912.09802 (2019) - 2018
- [c117]Jakub M. Tomczak, Max Welling:
VAE with a VampPrior. AISTATS 2018: 1214-1223 - [c116]Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling:
Modeling Relational Data with Graph Convolutional Networks. ESWC 2018: 593-607 - [c115]Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Spherical CNNs. ICLR 2018 - [c114]Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling:
HexaConv. ICLR (Poster) 2018 - [c113]Christos Louizos, Max Welling, Diederik P. Kingma:
Learning Sparse Neural Networks through L_0 Regularization. ICLR (Poster) 2018 - [c112]Peter O'Connor, Efstratios Gavves, Matthias Reisser, Max Welling:
Temporally Efficient Deep Learning with Spikes. ICLR (Poster) 2018 - [c111]Maximilian Ilse, Jakub M. Tomczak, Max Welling:
Attention-based Deep Multiple Instance Learning. ICML 2018: 2132-2141 - [c110]Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel:
Neural Relational Inference for Interacting Systems. ICML 2018: 2693-2702 - [c109]ChangYong Oh, Efstratios Gavves, Max Welling:
BOCK : Bayesian Optimization with Cylindrical Kernels. ICML 2018: 3865-3874 - [c108]Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling:
Rotation Equivariant CNNs for Digital Pathology. MICCAI (2) 2018: 210-218 - [c107]Raghavendra Selvan, Max Welling, Jesper Holst Pedersen, Jens Petersen, Marleen de Bruijne:
Mean Field Network Based Graph Refinement with Application to Airway Tree Extraction. MICCAI (2) 2018: 750-758 - [c106]Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang:
Graphical Generative Adversarial Networks. NeurIPS 2018: 6072-6083 - [c105]Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen:
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. NeurIPS 2018: 10402-10413 - [c104]Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. UAI 2018: 393-402 - [i79]Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Spherical CNNs. CoRR abs/1801.10130 (2018) - [i78]Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel:
Neural Relational Inference for Interacting Systems. CoRR abs/1802.04687 (2018) - [i77]Maximilian Ilse, Jakub M. Tomczak, Max Welling:
Attention-based Deep Multiple Instance Learning. CoRR abs/1802.04712 (2018) - [i76]Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling:
HexaConv. CoRR abs/1803.02108 (2018) - [i75]Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. CoRR abs/1803.05649 (2018) - [i74]Wouter Kool, Max Welling:
Attention Solves Your TSP. CoRR abs/1803.08475 (2018) - [i73]Raghavendra Selvan, Max Welling, Jesper Holst Pedersen, Jens Petersen, Marleen de Bruijne:
Mean Field Network based Graph Refinement with application to Airway Tree Extraction. CoRR abs/1804.03348 (2018) - [i72]Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang:
Graphical Generative Adversarial Networks. CoRR abs/1804.03429 (2018) - [i71]Raghavendra Selvan, Thomas N. Kipf, Max Welling, Jesper Holst Pedersen, Jens Petersen, Marleen de Bruijne:
Extraction of Airways using Graph Neural Networks. CoRR abs/1804.04436 (2018) - [i70]Mevlana Gemici, Zeynep Akata, Max Welling:
Primal-Dual Wasserstein GAN. CoRR abs/1805.09575 (2018) - [i69]ChangYong Oh, Efstratios Gavves, Max Welling:
BOCK : Bayesian Optimization with Cylindrical Kernels. CoRR abs/1806.01619 (2018) - [i68]Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling:
Rotation Equivariant CNNs for Digital Pathology. CoRR abs/1806.03962 (2018) - [i67]Jasper Linmans, Jim Winkens, Bastiaan S. Veeling, Taco S. Cohen, Max Welling:
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks. CoRR abs/1807.00583 (2018) - [i66]Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen:
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. CoRR abs/1807.02547 (2018) - [i65]Jorn W. T. Peters, Max Welling:
Probabilistic Binary Neural Networks. CoRR abs/1809.03368 (2018) - [i64]Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. CoRR abs/1810.01118 (2018) - [i63]Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, Max Welling:
Relaxed Quantization for Discretized Neural Networks. CoRR abs/1810.01875 (2018) - [i62]Bastiaan S. Veeling, Rianne van den Berg, Max Welling:
Predictive Uncertainty through Quantization. CoRR abs/1810.05500 (2018) - [i61]Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling:
The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models. CoRR abs/1810.06943 (2018) - [i60]Raghavendra Selvan, Thomas N. Kipf, Max Welling, Jesper Holst Pedersen, Jens Petersen, Marleen de Bruijne:
Graph Refinement based Tree Extraction using Mean-Field Networks and Graph Neural Networks. CoRR abs/1811.08674 (2018) - 2017
- [j26]A. Eck, Luisa M. Zintgraf, E. F. J. de Groot, Tim G. J. de Meij, Taco S. Cohen, P. H. M. Savelkoul, Max Welling, A. E. Budding:
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinform. 18(1): 441:1-441:13 (2017) - [c103]Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling:
DP-EM: Differentially Private Expectation Maximization. AISTATS 2017: 896-904 - [c102]Taco S. Cohen, Max Welling:
Steerable CNNs. ICLR (Poster) 2017 - [c101]Thomas N. Kipf, Max Welling:
Semi-Supervised Classification with Graph Convolutional Networks. ICLR (Poster) 2017 - [c100]Peter O'Connor, Max Welling:
Sigma Delta Quantized Networks. ICLR (Poster) 2017 - [c99]Karen Ullrich, Edward Meeds, Max Welling:
Soft Weight-Sharing for Neural Network Compression. ICLR (Poster) 2017 - [c98]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. ICLR (Poster) 2017 - [c97]Christos Louizos, Max Welling:
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. ICML 2017: 2218-2227 - [c96]Christos Louizos, Karen Ullrich, Max Welling:
Bayesian Compression for Deep Learning. NIPS 2017: 3288-3298 - [c95]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. NIPS 2017: 6446-6456 - [i59]Karen Ullrich, Edward Meeds, Max Welling:
Soft Weight-Sharing for Neural Network Compression. CoRR abs/1702.04008 (2017) - [i58]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. CoRR abs/1702.04595 (2017) - [i57]Christos Louizos, Max Welling:
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. CoRR abs/1703.01961 (2017) - [i56]Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling:
Modeling Relational Data with Graph Convolutional Networks. CoRR abs/1703.06103 (2017) - [i55]Jakub M. Tomczak, Max Welling:
VAE with a VampPrior. CoRR abs/1705.07120 (2017) - [i54]Christos Louizos, Karen Ullrich, Max Welling:
Bayesian Compression for Deep Learning. CoRR abs/1705.08665 (2017) - [i53]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. CoRR abs/1705.08821 (2017) - [i52]Rianne van den Berg, Thomas N. Kipf, Max Welling:
Graph Convolutional Matrix Completion. CoRR abs/1706.02263 (2017) - [i51]Patrick Putzky, Max Welling:
Recurrent Inference Machines for Solving Inverse Problems. CoRR abs/1706.04008 (2017) - [i50]Peter O'Connor, Efstratios Gavves, Max Welling:
Temporally Efficient Deep Learning with Spikes. CoRR abs/1706.04159 (2017) - [i49]Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Convolutional Networks for Spherical Signals. CoRR abs/1709.04893 (2017) - [i48]Jakub M. Tomczak, Maximilian Ilse, Max Welling:
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification. CoRR abs/1712.00310 (2017) - [i47]Christos Louizos, Max Welling, Diederik P. Kingma:
Learning Sparse Neural Networks through L0 Regularization. CoRR abs/1712.01312 (2017) - 2016
- [j25]Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. J. Mach. Learn. Res. 17: 10:1-10:29 (2016) - [j24]Anoop Korattikara, Yutian Chen, Max Welling:
Sequential Tests for Large-Scale Learning. Neural Comput. 28(1): 45-70 (2016) - [c94]Wenzhe Li, Sungjin Ahn, Max Welling:
Scalable MCMC for Mixed Membership Stochastic Blockmodels. AISTATS 2016: 723-731 - [c93]Christos Louizos, Max Welling:
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. ICML 2016: 1708-1716 - [c92]Taco Cohen, Max Welling:
Group Equivariant Convolutional Networks. ICML 2016: 2990-2999 - [c91]Ismail El-Helw, Rutger F. H. Hofman, Wenzhe Li, Sungjin Ahn, Max Welling, Henri E. Bal:
Scalable Overlapping Community Detection. IPDPS Workshops 2016: 1463-1472 - [c90]Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling:
Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS 2016: 4736-4744 - [c89]James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri:
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. UAI 2016 - [c88]Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel:
The Variational Fair Autoencoder. ICLR 2016 - [e10]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I. Lecture Notes in Computer Science 9905, Springer 2016, ISBN 978-3-319-46447-3 [contents] - [e9]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II. Lecture Notes in Computer Science 9906, Springer 2016, ISBN 978-3-319-46474-9 [contents] - [e8]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III. Lecture Notes in Computer Science 9907, Springer 2016, ISBN 978-3-319-46486-2 [contents] - [e7]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV. Lecture Notes in Computer Science 9908, Springer 2016, ISBN 978-3-319-46492-3 [contents] - [e6]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V. Lecture Notes in Computer Science 9909, Springer 2016, ISBN 978-3-319-46453-4 [contents] - [e5]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI. Lecture Notes in Computer Science 9910, Springer 2016, ISBN 978-3-319-46465-7 [contents] - [e4]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VII. Lecture Notes in Computer Science 9911, Springer 2016, ISBN 978-3-319-46477-0 [contents] - [e3]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII. Lecture Notes in Computer Science 9912, Springer 2016, ISBN 978-3-319-46483-1 [contents] - [i46]Yutian Chen, Max Welling:
Herding as a Learning System with Edge-of-Chaos Dynamics. CoRR abs/1602.03014 (2016) - [i45]Taco S. Cohen, Max Welling:
Group Equivariant Convolutional Networks. CoRR abs/1602.07576 (2016) - [i44]Peter O'Connor, Max Welling:
Deep Spiking Networks. CoRR abs/1602.08323 (2016) - [i43]Luisa M. Zintgraf, Taco S. Cohen, Max Welling:
A New Method to Visualize Deep Neural Networks. CoRR abs/1603.02518 (2016) - [i42]Christos Louizos, Max Welling:
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. CoRR abs/1603.04733 (2016) - [i41]James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri:
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. CoRR abs/1603.07294 (2016) - [i40]Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling:
Practical Privacy For Expectation Maximization. CoRR abs/1605.06995 (2016) - [i39]Mijung Park, Max Welling:
A note on privacy preserving iteratively reweighted least squares. CoRR abs/1605.07511 (2016) - [i38]Diederik P. Kingma, Tim Salimans, Max Welling:
Improving Variational Inference with Inverse Autoregressive Flow. CoRR abs/1606.04934 (2016) - [i37]Thomas N. Kipf, Max Welling:
Semi-Supervised Classification with Graph Convolutional Networks. CoRR abs/1609.02907 (2016) - [i36]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Private Topic Modeling. CoRR abs/1609.04120 (2016) - [i35]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Variational Bayes In Private Settings (VIPS). CoRR abs/1611.00340 (2016) - [i34]Peter O'Connor, Max Welling:
Sigma Delta Quantized Networks. CoRR abs/1611.02024 (2016) - [i33]Thomas N. Kipf, Max Welling:
Variational Graph Auto-Encoders. CoRR abs/1611.07308 (2016) - [i32]Jakub M. Tomczak, Max Welling:
Improving Variational Auto-Encoders using Householder Flow. CoRR abs/1611.09630 (2016) - [i31]Taco S. Cohen, Max Welling:
Steerable CNNs. CoRR abs/1612.08498 (2016) - [i30]Max Welling:
Marrying Graphical Models with Deep Learning. ERCIM News 2016(107) (2016) - 2015
- [j23]Edward Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John S. Lowengrub, Max Welling:
POPE: post optimization posterior evaluation of likelihood free models. BMC Bioinform. 16: 264:1-264:20 (2015) - [j22]Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling:
MLitB: machine learning in the browser. PeerJ Comput. Sci. 1: e11 (2015) - [c87]Tim Salimans, Diederik P. Kingma, Max Welling:
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. ICML 2015: 1218-1226 - [c86]Taco Cohen, Max Welling:
Harmonic Exponential Families on Manifolds. ICML 2015: 1757-1765 - [c85]Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling:
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. KDD 2015: 9-18 - [c84]Edward Meeds, Max Welling:
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. NIPS 2015: 2080-2088 - [c83]Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. NIPS 2015: 2575-2583 - [c82]Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling:
Bayesian dark knowledge. NIPS 2015: 3438-3446 - [c81]Edward Meeds, Robert Leenders, Max Welling:
Hamiltonian ABC. UAI 2015: 582-591 - [c80]Taco S. Cohen, Max Welling:
Transformation Properties of Learned Visual Representations. ICLR (Poster) 2015 - [i29]Sungjin Ahn, Anoop Korattikara Balan, Nathan Liu, Suju Rajan, Max Welling:
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. CoRR abs/1503.01596 (2015) - [i28]Edward Meeds, Robert Leenders, Max Welling:
Hamiltonian ABC. CoRR abs/1503.01916 (2015) - [i27]Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. CoRR abs/1506.02557 (2015) - [i26]Edward Meeds, Max Welling:
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. CoRR abs/1506.03693 (2015) - [i25]Anoop Korattikara Balan, Vivek Rathod, Kevin Murphy, Max Welling:
Bayesian Dark Knowledge. CoRR abs/1506.04416 (2015) - [i24]Wenzhe Li, Sungjin Ahn, Max Welling:
Scalable MCMC for Mixed Membership Stochastic Blockmodels. CoRR abs/1510.04815 (2015) - 2014
- [c79]Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth:
Approximate Slice Sampling for Bayesian Posterior Inference. AISTATS 2014: 185-193 - [c78]Anoop Korattikara Balan, Yutian Chen, Max Welling:
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. ICML 2014: 181-189 - [c77]Sungjin Ahn, Babak Shahbaba, Max Welling:
Distributed Stochastic Gradient MCMC. ICML 2014: 1044-1052 - [c76]Taco Cohen, Max Welling:
Learning the Irreducible Representations of Commutative Lie Groups. ICML 2014: 1755-1763 - [c75]Diederik P. Kingma, Max Welling:
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. ICML 2014: 1782-1790 - [c74]Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling:
Semi-supervised Learning with Deep Generative Models. NIPS 2014: 3581-3589 - [c73]Edward Meeds, Max Welling:
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. UAI 2014: 593-602 - [c72]Diederik P. Kingma, Max Welling:
Auto-Encoding Variational Bayes. ICLR 2014 - [e2]Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. 2014 [contents] - [i23]Edward Meeds, Max Welling:
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. CoRR abs/1401.2838 (2014) - [i22]Diederik P. Kingma, Max Welling:
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. CoRR abs/1402.0480 (2014) - [i21]Taco Cohen, Max Welling:
Learning the Irreducible Representations of Commutative Lie Groups. CoRR abs/1402.4437 (2014) - [i20]Max Welling:
Exploiting the Statistics of Learning and Inference. CoRR abs/1402.7025 (2014) - [i19]Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling:
Semi-Supervised Learning with Deep Generative Models. CoRR abs/1406.5298 (2014) - [i18]Yutian Chen, Max Welling:
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior. CoRR abs/1408.2047 (2014) - [i17]Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling:
MLitB: Machine Learning in the Browser. CoRR abs/1412.2432 (2014) - 2013
- [c71]Sungjin Ahn, Yutian Chen, Max Welling:
Distributed and Adaptive Darting Monte Carlo through Regenerations. AISTATS 2013: 108-116 - [c70]Yutian Chen, Max Welling:
Evidence Estimation for Bayesian Partially Observed MRFs. AISTATS 2013: 178-186 - [c69]Peter Welinder, Max Welling, Pietro Perona:
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration. CVPR 2013: 3262-3269 - [c68]James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling:
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. KDD 2013: 446-454 - [c67]Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. ICLR 2013 - [i16]Max Welling, Yee Whye Teh:
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation. CoRR abs/1301.2317 (2013) - [i15]Anoop Korattikara Balan, Yutian Chen, Max Welling:
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. CoRR abs/1304.5299 (2013) - [i14]James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling:
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation. CoRR abs/1305.2452 (2013) - 2012
- [j21]Sing Bing Kang, Jiri Matas, Max Welling, Ramin Zabih:
State of the Journal. IEEE Trans. Pattern Anal. Mach. Intell. 34(1): 1-2 (2012) - [j20]Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 34(2): 209-210 (2012) - [j19]Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 34(5): 833 (2012) - [j18]Xiangxin Zhu, Max Welling, Fang Jin, John S. Lowengrub:
Predicting simulation parameters of biological systems using a Gaussian process model. Stat. Anal. Data Min. 5(6): 509-522 (2012) - [c66]Sungjin Ahn, Anoop Korattikara Balan, Max Welling:
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring. ICML 2012 - [c65]Max Welling, Ian Porteous, Kenichi Kurihara:
Exchangeable inconsistent priors for Bayesian posterior inference. ITA 2012: 407-414 - [c64]Levi Boyles, Max Welling:
The Time-Marginalized Coalescent Prior for Hierarchical Clustering. NIPS 2012: 2978-2986 - [c63]Yutian Chen, Max Welling:
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior. UAI 2012: 174-184 - [c62]Andrew Gelfand, Max Welling:
Generalized Belief Propagation on Tree Robust Structured Region Graphs. UAI 2012: 296-305 - [c61]Max Welling, Andrew Gelfand, Alexander Ihler:
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation. UAI 2012: 883-892 - [c60]Dilan Görür, Levi Boyles, Max Welling:
Scalable Inference on Kingman's Coalescent using Pair Similarity . AISTATS 2012: 440-448 - [i13]Yutian Chen, Max Welling, Alexander J. Smola:
Super-Samples from Kernel Herding. CoRR abs/1203.3472 (2012) - [i12]Max Welling:
Herding Dynamic Weights for Partially Observed Random Field Models. CoRR abs/1205.2605 (2012) - [i11]Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh:
On Smoothing and Inference for Topic Models. CoRR abs/1205.2662 (2012) - [i10]Yutian Chen, Max Welling:
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior. CoRR abs/1206.1088 (2012) - [i9]Max Welling, Yee Whye Teh, Hilbert J. Kappen:
Hybrid Variational/Gibbs Collapsed Inference in Topic Models. CoRR abs/1206.3297 (2012) - [i8]Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling:
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. CoRR abs/1206.6845 (2012) - [i7]Max Welling, Sridevi Parise:
Bayesian Random Fields: The Bethe-Laplace Approximation. CoRR abs/1206.6868 (2012) - [i6]Max Welling, Thomas P. Minka, Yee Whye Teh:
Structured Region Graphs: Morphing EP into GBP. CoRR abs/1207.1426 (2012) - [i5]Max Welling:
On the Choice of Regions for Generalized Belief Propagation. CoRR abs/1207.4158 (2012) - [i4]Peter Welinder, Max Welling, Pietro Perona:
Semisupervised Classifier Evaluation and Recalibration. CoRR abs/1210.2162 (2012) - [i3]Andrew Gelfand, Max Welling:
Generalized Belief Propagation on Tree Robust Structured Region Graphs. CoRR abs/1210.4857 (2012) - [i2]Max Welling, Andrew E. Gelfand, Alexander Ihler:
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation. CoRR abs/1210.4916 (2012) - [i1]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation. CoRR abs/1212.2513 (2012) - 2011
- [j17]Ramin Zabih, Sing Bing Kang, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 33(11): 2129-2130 (2011) - [j16]Evgeniy Bart, Max Welling, Pietro Perona:
Unsupervised Organization of Image Collections: Taxonomies and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 33(11): 2302-2315 (2011) - [j15]Ramin Zabih, Sing Bing Kang, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 33(12): 2337-2340 (2011) - [j14]Cristian Sminchisescu, Max Welling:
Generalized darting Monte Carlo. Pattern Recognit. 44(10-11): 2738-2748 (2011) - [c59]Yutian Chen, Andrew Gelfand, Charless C. Fowlkes, Max Welling:
Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation. ICCV 2011: 2635-2642 - [c58]Max Welling, Yee Whye Teh:
Bayesian Learning via Stochastic Gradient Langevin Dynamics. ICML 2011: 681-688 - [c57]Levi Boyles, Anoop Korattikara Balan, Deva Ramanan, Max Welling:
Statistical Tests for Optimization Efficiency. NIPS 2011: 2196-2204 - [c56]Anoop Korattikara Balan, Levi Boyles, Max Welling, Jingu Kim, Haesun Park:
Statistical Optimization of Non-Negative Matrix Factorization. AISTATS 2011: 128-136 - [c55]Laurens van der Maaten, Max Welling, Lawrence K. Saul:
Hidden-Unit Conditional Random Fields. AISTATS 2011: 479-488 - 2010
- [j13]Yuan Zhang, Lichun Bao, Shih-Hsien Yang, Max Welling, Di Wu:
Localization Algorithms for Wireless Sensor Retrieval. Comput. J. 53(10): 1594-1605 (2010) - [c54]Ian Porteous, Arthur U. Asuncion, Max Welling:
Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures. AAAI 2010: 563-568 - [c53]Yutian Chen, Max Welling:
Dynamical Products of Experts for Modeling Financial Time Series. ICML 2010: 207-214 - [c52]Andrew Gelfand, Yutian Chen, Laurens van der Maaten, Max Welling:
On Herding and the Perceptron Cycling Theorem. NIPS 2010: 694-702 - [c51]Yutian Chen, Max Welling, Alexander J. Smola:
Super-Samples from Kernel Herding. UAI 2010: 109-116 - [c50]Yutian Chen, Max Welling:
Parametric Herding. AISTATS 2010: 97-104
2000 – 2009
- 2009
- [j12]David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Distributed Algorithms for Topic Models. J. Mach. Learn. Res. 10: 1801-1828 (2009) - [j11]Kenichi Kurihara, Max Welling:
Bayesian k-Means as a "Maximization-Expectation" Algorithm. Neural Comput. 21(4): 1145-1172 (2009) - [c49]Max Welling:
Herding dynamical weights to learn. ICML 2009: 1121-1128 - [c48]Yutian Chen, Max Welling:
Bayesian Extreme Components Analysis. IJCAI 2009: 1022-1027 - [c47]Yuan Zhang, Lichun Bao, Max Welling, Shih-Hsien Yang:
Base Station Localization in Search of Empty Spectrum Spaces in Cognitive Radio Networks. MSN 2009: 94-101 - [c46]Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh:
On Smoothing and Inference for Topic Models. UAI 2009: 27-34 - [c45]Max Welling:
Herding Dynamic Weights for Partially Observed Random Field Models. UAI 2009: 599-606 - [c44]David A. Van Dyk, Max Welling:
Preface. AISTATS 2009 - [e1]David A. Van Dyk, Max Welling:
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, AISTATS 2009, Clearwater Beach, Florida, USA, April 16-18, 2009. JMLR Proceedings 5, JMLR.org 2009 [contents] - 2008
- [j10]Alex Holub, Max Welling, Pietro Perona:
Hybrid Generative-Discriminative Visual Categorization. Int. J. Comput. Vis. 77(1-3): 239-258 (2008) - [c43]Ian Porteous, Evgeniy Bart, Max Welling:
Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization. AAAI 2008: 1487-1490 - [c42]Evgeniy Bart, Ian Porteous, Pietro Perona, Max Welling:
Unsupervised learning of visual taxonomies. CVPR 2008 - [c41]Ryan Gomes, Max Welling, Pietro Perona:
Incremental learning of nonparametric Bayesian mixture models. CVPR 2008 - [c40]Ryan Gomes, Max Welling, Pietro Perona:
Memory bounded inference in topic models. ICML 2008: 344-351 - [c39]Ian Porteous, David Newman, Alexander Ihler, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Fast collapsed gibbs sampling for latent dirichlet allocation. KDD 2008: 569-577 - [c38]Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Asynchronous Distributed Learning of Topic Models. NIPS 2008: 81-88 - [c37]Max Welling, Chaitanya Chemudugunta, Nathan Sutter:
Deterministic Latent Variable Models and Their Pitfalls. SDM 2008: 196-207 - [c36]Max Welling, Yee Whye Teh, Bert Kappen:
Hybrid Variational/Gibbs Collapsed Inference in Topic Models. UAI 2008: 587-594 - 2007
- [j9]Max Welling:
Product of experts. Scholarpedia 2(10): 3879 (2007) - [c35]Max Welling, Joseph J. Lim:
A Distributed Message Passing Algorithm for Sensor Localization. ICANN (1) 2007: 767-775 - [c34]Kenichi Kurihara, Max Welling, Yee Whye Teh:
Collapsed Variational Dirichlet Process Mixture Models. IJCAI 2007: 2796-2801 - [c33]David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Distributed Inference for Latent Dirichlet Allocation. NIPS 2007: 1081-1088 - [c32]Yee Whye Teh, Kenichi Kurihara, Max Welling:
Collapsed Variational Inference for HDP. NIPS 2007: 1481-1488 - [c31]Max Welling, Ian Porteous, Evgeniy Bart:
Infinite State Bayes-Nets for Structured Domains. NIPS 2007: 1601-1608 - [c30]Cristian Sminchisescu, Max Welling:
Generalized Darting Monte Carlo. AISTATS 2007: 516-523 - 2006
- [j8]Geoffrey E. Hinton, Simon Osindero, Max Welling, Yee Whye Teh:
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation. Cogn. Sci. 30(4): 725-731 (2006) - [j7]Simon Osindero, Max Welling, Geoffrey E. Hinton:
Topographic Product Models Applied to Natural Scene Statistics. Neural Comput. 18(2): 381-414 (2006) - [c29]Peter V. Gehler, Alex Holub, Max Welling:
The rate adapting poisson model for information retrieval and object recognition. ICML 2006: 337-344 - [c28]Kenichi Kurihara, Max Welling, Nikos Vlassis:
Accelerated Variational Dirichlet Process Mixtures. NIPS 2006: 761-768 - [c27]Sridevi Parise, Max Welling:
Bayesian Model Scoring in Markov Random Fields. NIPS 2006: 1073-1080 - [c26]Yee Whye Teh, David Newman, Max Welling:
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. NIPS 2006: 1353-1360 - [c25]Max Welling, Kenichi Kurihara:
Bayesian K-Means as a "Maximization-Expectation" Algorithm. SDM 2006: 474-478 - [c24]Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling:
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. UAI 2006 - [c23]Max Welling, Sridevi Parise:
Bayesian Random Fields: The Bethe-Laplace Approximation. UAI 2006 - 2005
- [c22]Max Welling:
An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions. AISTATS 2005: 389-396 - [c21]Max Welling, Charles Sutton:
Learning in Markov Random Fields with Contrastive Free Energies. AISTATS 2005: 397-404 - [c20]Max Welling:
Robust Higher Order Statistics. AISTATS 2005: 405-412 - [c19]Alex Holub, Max Welling, Pietro Perona:
Combining Generative Models and Fisher Kernels for Object Recognition. ICCV 2005: 136-143 - [c18]Peter V. Gehler, Max Welling:
Products of Edge-perts. NIPS 2005: 419-426 - [c17]Max Welling, Thomas P. Minka, Yee Whye Teh:
Structured Region Graphs: Morphing EP into GBP. UAI 2005: 609-614 - 2004
- [j6]Max Welling, Yee Whye Teh:
Linear Response Algorithms for Approximate Inference in Graphical Models. Neural Comput. 16(1): 197-221 (2004) - [j5]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Probabilistic sequential independent components analysis. IEEE Trans. Neural Networks 15(4): 838-849 (2004) - [c16]Max Welling, Michal Rosen-Zvi, Yee Whye Teh:
Approximate inference by Markov chains on union spaces. ICML 2004 - [c15]Max Welling, Michal Rosen-Zvi, Geoffrey E. Hinton:
Exponential Family Harmoniums with an Application to Information Retrieval. NIPS 2004: 1481-1488 - [c14]Max Welling:
On the Choice of Regions for Generalized Belief Propagation. UAI 2004: 585-592 - 2003
- [j4]Max Welling, Yee Whye Teh:
Approximate inference in Boltzmann machines. Artif. Intell. 143(1): 19-50 (2003) - [j3]Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton:
Energy-Based Models for Sparse Overcomplete Representations. J. Mach. Learn. Res. 4: 1235-1260 (2003) - [c13]Yee Whye Teh, Max Welling:
On Improving the Efficiency of the Iterative Proportional Fitting Procedure. AISTATS 2003: 262-269 - [c12]Max Welling, Felix V. Agakov, Christopher K. I. Williams:
Extreme Components Analysis. NIPS 2003: 137-144 - [c11]Max Welling, Yee Whye Teh:
Linear Response for Approximate Inference. NIPS 2003: 361-368 - [c10]Geoffrey E. Hinton, Max Welling, Andriy Mnih:
Wormholes Improve Contrastive Divergence. NIPS 2003: 417-424 - [c9]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation. UAI 2003: 575-582 - 2002
- [c8]Max Welling, Geoffrey E. Hinton:
A New Learning Algorithm for Mean Field Boltzmann Machines. ICANN 2002: 351-357 - [c7]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Self Supervised Boosting. NIPS 2002: 665-672 - [c6]Max Welling, Geoffrey E. Hinton, Simon Osindero:
Learning Sparse Topographic Representations with Products of Student-t Distributions. NIPS 2002: 1359-1366 - 2001
- [j2]Max Welling, Markus Weber:
A Constrained EM Algorithm for Independent Component Analysis. Neural Comput. 13(3): 677-689 (2001) - [j1]Max Welling, Markus Weber:
Positive tensor factorization. Pattern Recognit. Lett. 22(12): 1255-1261 (2001) - [c5]Yee Whye Teh, Max Welling:
The Unified Propagation and Scaling Algorithm. NIPS 2001: 953-960 - [c4]Max Welling, Yee Whye Teh:
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation. UAI 2001: 554-561 - 2000
- [c3]Markus Weber, Max Welling, Pietro Perona:
Towards Automatic Discovery of Object Categories. CVPR 2000: 2101- - [c2]Markus Weber, Max Welling, Pietro Perona:
Unsupervised Learning of Models for Recognition. ECCV (1) 2000: 18-32 - [c1]Markus Weber, Wolfgang Einhäuser, Max Welling, Pietro Perona:
Viewpoint-Invariant Learning and Detection of Human Heads. FG 2000: 20-27
Coauthor Index
Anoop Korattikara Balan
aka: Anoop Korattikara
aka: Anoop Korattikara
Taco Cohen
aka: Taco S. Cohen
aka: Taco S. Cohen
Thomas Kipf
aka: Thomas N. Kipf
aka: Thomas N. Kipf
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Citation data
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load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
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last updated on 2025-04-23 21:04 CEST by the dblp team
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