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2020 – today
- 2020
[j33]Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Branch and Bound for Piecewise Linear Neural Network Verification. J. Mach. Learn. Res. 21: 42:1-42:39 (2020)- [j32]Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Zídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis:
Improved protein structure prediction using potentials from deep learning. Nat. 577(7792): 706-710 (2020) - [c177]Sven Gowal, Chongli Qin, Po-Sen Huang, A. Taylan Cemgil, Krishnamurthy Dvijotham, Timothy A. Mann, Pushmeet Kohli:
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations. CVPR 2020: 1208-1217 - [c176]Daniel Zoran, Mike Chrzanowski, Po-Sen Huang, Sven Gowal, Alex Mott, Pushmeet Kohli:
Towards Robust Image Classification Using Sequential Attention Models. CVPR 2020: 9480-9489 - [c175]Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, Pushmeet Kohli:
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation. EMNLP (Findings) 2020: 65-83 - [c174]A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy (Dj) Dvijotham, Pushmeet Kohli:
Adversarially Robust Representations with Smooth Encoders. ICLR 2020 - [c173]Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, Zico Kolter, Chongli Qin, András György, Kai Xiao, Sven Gowal, Pushmeet Kohli:
A Framework for robustness Certification of Smoothed Classifiers using F-Divergences. ICLR 2020 - [c172]Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals:
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs. ICLR 2020 - [c171]Johannes Welbl, Po-Sen Huang, Robert Stanforth, Sven Gowal, Krishnamurthy (Dj) Dvijotham, Martin Szummer, Pushmeet Kohli:
Towards Verified Robustness under Text Deletion Interventions. ICLR 2020 - [c170]Tsui-Wei Weng, Krishnamurthy (Dj) Dvijotham, Jonathan Uesato, Kai Xiao, Sven Gowal, Robert Stanforth, Pushmeet Kohli:
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control. ICLR 2020 - [c169]Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum:
CLEVRER: Collision Events for Video Representation and Reasoning. ICLR 2020 - [c168]A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Sven Gowal, Pushmeet Kohli:
The Autoencoding Variational Autoencoder. NeurIPS 2020 - [c167]Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli:
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. NeurIPS 2020 - [c166]Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli:
Training Generative Adversarial Networks by Solving Ordinary Differential Equations. NeurIPS 2020 - [c165]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. UAI 2020: 370-379 - [i97]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. CoRR abs/2002.10410 (2020) - [i96]Yujia Li, Felix Gimeno, Pushmeet Kohli, Oriol Vinyals:
Strong Generalization and Efficiency in Neural Programs. CoRR abs/2007.03629 (2020) - [i95]Richard Evans, José Hernández-Orallo, Johannes Welbl, Pushmeet Kohli, Marek J. Sergot:
Evaluating the Apperception Engine. CoRR abs/2007.05367 (2020) - [i94]Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R. Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, A. Taylan Cemgil, S. M. Ali Eslami, Olaf Ronneberger:
Contrastive Training for Improved Out-of-Distribution Detection. CoRR abs/2007.05566 (2020) - [i93]Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy A. Mann, Pushmeet Kohli:
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples. CoRR abs/2010.03593 (2020) - [i92]Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli:
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. CoRR abs/2010.11645 (2020) - [i91]Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli:
Training Generative Adversarial Networks by Solving Ordinary Differential Equations. CoRR abs/2010.15040 (2020) - [i90]Jamie Hayes, Krishnamurthy Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande:
Towards transformation-resilient provenance detection of digital media. CoRR abs/2011.07355 (2020) - [i89]A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Sven Gowal, Pushmeet Kohli:
Autoencoding Variational Autoencoder. CoRR abs/2012.03715 (2020) - [i88]Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, Yori Zwols:
Solving Mixed Integer Programs Using Neural Networks. CoRR abs/2012.13349 (2020)
2010 – 2019
- 2019
- [j31]Danhang Tang
, Qi Ye, Shanxin Yuan, Jonathan Taylor, Pushmeet Kohli, Cem Keskin, Tae-Kyun Kim, Jamie Shotton:
Opening the Black Box: Hierarchical Sampling Optimization for Hand Pose Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 41(9): 2161-2175 (2019) - [j30]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. SIAM J. Imaging Sci. 12(1): 287-318 (2019) - [c164]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli:
Degenerate Feedback Loops in Recommender Systems. AIES 2019: 383-390 - [c163]Dylan Banarse, Yoram Bachrach, Siqi Liu, Guy Lever, Nicolas Heess, Chrisantha Fernando, Pushmeet Kohli, Thore Graepel:
The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution. AAMAS 2019: 1134-1142 - [c162]Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli:
Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications. CVPR 2019: 12260-12269 - [c161]Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, Pushmeet Kohli:
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation. EMNLP/IJCNLP (1) 2019: 4081-4091 - [c160]Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Arthur Mann, Pushmeet Kohli:
Scalable Verified Training for Provably Robust Image Classification. ICCV 2019: 4841-4850 - [c159]Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Seyed Arian Hosseini, Pushmeet Kohli, Edward Grefenstette:
Learning to Understand Goal Specifications by Modelling Reward. ICLR (Poster) 2019 - [c158]Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu:
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision. ICLR 2019 - [c157]Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier:
Value Propagation Networks. ICLR (Poster) 2019 - [c156]Chongli Qin, Krishnamurthy (Dj) Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli:
Verification of Non-Linear Specifications for Neural Networks. ICLR (Poster) 2019 - [c155]David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli:
Analysing Mathematical Reasoning Abilities of Neural Models. ICLR (Poster) 2019 - [c154]Jonathan Uesato, Ananya Kumar, Csaba Szepesvári, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy (Dj) Dvijotham, Nicolas Heess, Pushmeet Kohli:
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures. ICLR (Poster) 2019 - [c153]Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick:
Structured agents for physical construction. ICML 2019: 464-474 - [c152]Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
CompILE: Compositional Imitation Learning and Execution. ICML 2019: 3418-3428 - [c151]Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli:
Graph Matching Networks for Learning the Similarity of Graph Structured Objects. ICML 2019: 3835-3845 - [c150]Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Timothy A. Mann, Pushmeet Kohli:
A Dual Approach to Verify and Train Deep Networks. IJCAI 2019: 6156-6160 - [c149]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. NeurIPS 2019: 2514-2525 - [c148]Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, Pushmeet Kohli:
Are Labels Required for Improving Adversarial Robustness? NeurIPS 2019: 12192-12202 - [c147]Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli:
Adversarial Robustness through Local Linearization. NeurIPS 2019: 13824-13833 - [c146]Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli:
Efficient Neural Network Verification with Exactness Characterization. UAI 2019: 497-507 - [i87]Chongli Qin, Krishnamurthy (Dj) Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli:
Verification of Non-Linear Specifications for Neural Networks. CoRR abs/1902.09592 (2019) - [i86]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli:
Degenerate Feedback Loops in Recommender Systems. CoRR abs/1902.10730 (2019) - [i85]Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh:
Meta-Learning surrogate models for sequential decision making. CoRR abs/1903.11907 (2019) - [i84]David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli:
Analysing Mathematical Reasoning Abilities of Neural Models. CoRR abs/1904.01557 (2019) - [i83]Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick:
Structured agents for physical construction. CoRR abs/1904.03177 (2019) - [i82]Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli:
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications. CoRR abs/1904.12004 (2019) - [i81]Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu:
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision. CoRR abs/1904.12584 (2019) - [i80]Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli:
Graph Matching Networks for Learning the Similarity of Graph Structured Objects. CoRR abs/1904.12787 (2019) - [i79]Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals:
REGAL: Transfer Learning For Fast Optimization of Computation Graphs. CoRR abs/1905.02494 (2019) - [i78]Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger:
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities. CoRR abs/1905.13077 (2019) - [i77]Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli:
Are Labels Required for Improving Adversarial Robustness? CoRR abs/1905.13725 (2019) - [i76]Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli:
Adversarial Robustness through Local Linearization. CoRR abs/1907.02610 (2019) - [i75]Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, Pushmeet Kohli:
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation. CoRR abs/1909.01492 (2019) - [i74]Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Branch and Bound for Piecewise Linear Neural Network Verification. CoRR abs/1909.06588 (2019) - [i73]Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum:
CLEVRER: CoLlision Events for Video REpresentation and Reasoning. CoRR abs/1910.01442 (2019) - [i72]Richard Evans, José Hernández-Orallo, Johannes Welbl, Pushmeet Kohli, Marek J. Sergot:
Making sense of sensory input. CoRR abs/1910.02227 (2019) - [i71]Sven Gowal, Jonathan Uesato, Chongli Qin, Po-Sen Huang, Timothy A. Mann, Pushmeet Kohli:
An Alternative Surrogate Loss for PGD-based Adversarial Testing. CoRR abs/1910.09338 (2019) - [i70]Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. CoRR abs/1910.12980 (2019) - [i69]Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, Pushmeet Kohli:
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation. CoRR abs/1911.03064 (2019) - [i68]Daniel Zoran, Mike Chrzanowski, Po-Sen Huang, Sven Gowal, Alex Mott, Pushmeet Kohli:
Towards Robust Image Classification Using Sequential Attention Models. CoRR abs/1912.02184 (2019) - [i67]Sven Gowal, Chongli Qin, Po-Sen Huang, A. Taylan Cemgil, Krishnamurthy Dvijotham, Timothy A. Mann, Pushmeet Kohli:
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations. CoRR abs/1912.03192 (2019) - 2018
- [c145]Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka:
Batched Large-scale Bayesian Optimization in High-dimensional Spaces. AISTATS 2018: 745-754 - [c144]Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette:
Jointly Learning "What" and "How" from Instructions and Goal-States. ICLR (Workshop) 2018 - [c143]Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli:
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. ICLR (Poster) 2018 - [c142]Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette:
Can Neural Networks Understand Logical Entailment? ICLR (Poster) 2018 - [c141]Jonathan Uesato, Brendan O'Donoghue, Pushmeet Kohli, Aäron van den Oord:
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks. ICML 2018: 5032-5041 - [c140]Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri:
Programmatically Interpretable Reinforcement Learning. ICML 2018: 5052-5061 - [c139]Sahil Bhatia, Pushmeet Kohli, Rishabh Singh:
Neuro-symbolic program corrector for introductory programming assignments. ICSE 2018: 60-70 - [c138]Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Josh Tenenbaum:
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. NeurIPS 2018: 1039-1050 - [c137]Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, Pawan Kumar Mudigonda:
A Unified View of Piecewise Linear Neural Network Verification. NeurIPS 2018: 4795-4804 - [c136]Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A. Mann, Pushmeet Kohli:
A Dual Approach to Scalable Verification of Deep Networks. UAI 2018: 550-559 - [i66]Jonathan Uesato, Brendan O'Donoghue, Aäron van den Oord, Pushmeet Kohli:
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks. CoRR abs/1802.05666 (2018) - [i65]Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette:
Can Neural Networks Understand Logical Entailment? CoRR abs/1802.08535 (2018) - [i64]Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A. Mann, Pushmeet Kohli:
A Dual Approach to Scalable Verification of Deep Networks. CoRR abs/1803.06567 (2018) - [i63]Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri:
Programmatically Interpretable Reinforcement Learning. CoRR abs/1804.02477 (2018) - [i62]Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli:
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis. CoRR abs/1805.04276 (2018) - [i61]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials. CoRR abs/1805.09028 (2018) - [i60]Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelovic, Brendan O'Donoghue, Jonathan Uesato, Pushmeet Kohli:
Training verified learners with learned verifiers. CoRR abs/1805.10265 (2018) - [i59]Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier:
Value Propagation Networks. CoRR abs/1805.11199 (2018) - [i58]Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu:
Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018) - [i57]Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette:
Learning to Follow Language Instructions with Adversarial Reward Induction. CoRR abs/1806.01946 (2018) - [i56]Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Joshua B. Tenenbaum:
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. CoRR abs/1810.02338 (2018) - [i55]Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy A. Mann, Pushmeet Kohli:
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models. CoRR abs/1810.12715 (2018) - [i54]Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli:
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles. CoRR abs/1811.09300 (2018) - [i53]Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
Compositional Imitation Learning: Explaining and executing one task at a time. CoRR abs/1812.01483 (2018) - [i52]Jonathan Uesato, Ananya Kumar, Csaba Szepesvári, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy Dvijotham, Nicolas Heess, Pushmeet Kohli:
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures. CoRR abs/1812.01647 (2018) - [i51]Krishnamurthy Dvijotham, Marta Garnelo, Alhussein Fawzi, Pushmeet Kohli:
Verification of deep probabilistic models. CoRR abs/1812.02795 (2018) - [i50]Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli:
Scaling shared model governance via model splitting. CoRR abs/1812.05979 (2018) - [i49]Nils Jansen, Joost-Pieter Katoen, Pushmeet Kohli, Jan Kretínský:
Machine Learning and Model Checking Join Forces (Dagstuhl Seminar 18121). Dagstuhl Reports 8(3): 74-93 (2018) - 2017
- [c135]Jiajun Wu, Joshua B. Tenenbaum, Pushmeet Kohli:
Neural Scene De-rendering. CVPR 2017: 7035-7043 - [c134]Yinda Zhang, Mingru Bai, Pushmeet Kohli, Shahram Izadi, Jianxiong Xiao:
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding. ICCV 2017: 1201-1210 - [c133]Chen Liu, Jiajun Wu, Pushmeet Kohli, Yasutaka Furukawa:
Raster-to-Vector: Revisiting Floorplan Transformation. ICCV 2017: 2214-2222 - [c132]Kyle Olszewski, Zimo Li, Chao Yang, Yi Zhou, Ronald Yu, Zeng Huang, Sitao Xiang, Shunsuke Saito, Pushmeet Kohli, Hao Li:
Realistic Dynamic Facial Textures from a Single Image Using GANs. ICCV 2017: 5439-5448 - [c131]Lukasz Romaszko, Christopher K. I. Williams, Pol Moreno, Pushmeet Kohli:
Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image. ICCV Workshops 2017: 940-948 - [c130]Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs. ICLR (Poster) 2017 - [c129]Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli:
Neuro-Symbolic Program Synthesis. ICLR (Poster) 2017 - [c128]Yingzhen Yang, Jiahui Yu, Pushmeet Kohli, Jianchao Yang, Thomas S. Huang:
Support Regularized Sparse Coding and Its Fast Encoder. ICLR (Poster) 2017 - [c127]Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton:
Learning Continuous Semantic Representations of Symbolic Expressions. ICML 2017: 80-88 - [c126]Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
RobustFill: Neural Program Learning under Noisy I/O. ICML 2017: 990-998 - [c125]Jakob N. Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson:
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. ICML 2017: 1146-1155 - [c124]Junhyuk Oh, Satinder P. Singh, Honglak Lee, Pushmeet Kohli:
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. ICML 2017: 2661-2670 - [c123]Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli:
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. ICML 2017: 3656-3664 - [c122]Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum:
Learning to See Physics via Visual De-animation. NIPS 2017: 153-164 - [c121]Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew J. Hausknecht, Pushmeet Kohli:
Neural Program Meta-Induction. NIPS 2017: 2080-2088 - [c120]Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr:
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. NIPS 2017: 5925-5935 - [c119]Marc Brockschmidt, Yuxin Chen, Pushmeet Kohli, Siddharth Krishna, Daniel Tarlow:
Learning Shape Analysis. SAS 2017: 66-87 - [c118]Rishabh Singh, Pushmeet Kohli:
AP: Artificial Programming. SNAPL 2017: 16:1-16:12 - [i48]Jakob N. Foerster, Nantas Nardelli, Gregory Farquhar, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson:
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. CoRR abs/1702.08887 (2017) - [i47]Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli:
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning. CoRR abs/1703.01973 (2017) - [i46]Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
RobustFill: Neural Program Learning under Noisy I/O. CoRR abs/1703.07469 (2017) - [i45]Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli:
Deep API Programmer: Learning to Program with APIs. CoRR abs/1704.04327 (2017) - [i44]N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr:
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. CoRR abs/1706.00400 (2017) - [i43]Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka:
Batched Large-scale Bayesian Optimization in High-dimensional Spaces. CoRR abs/1706.01445 (2017) - [i42]Junhyuk Oh, Satinder P. Singh, Honglak Lee, Pushmeet Kohli:
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. CoRR abs/1706.05064 (2017) - [i41]Jacob Devlin, Rudy Bunel, Rishabh Singh, Matthew J. Hausknecht, Pushmeet Kohli:
Neural Program Meta-Induction. CoRR abs/1710.04157 (2017) - [i40]Jacob Devlin, Jonathan Uesato, Rishabh Singh, Pushmeet Kohli:
Semantic Code Repair using Neuro-Symbolic Transformation Networks. CoRR abs/1710.11054 (2017) - [i39]Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Piecewise Linear Neural Network verification: A comparative study. CoRR abs/1711.00455 (2017) - 2016
- [j29]Matteo Venanzi, John Guiver, Pushmeet Kohli, Nicholas R. Jennings:
Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems. J. Artif. Intell. Res. 56: 517-545 (2016) - [j28]Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip L. Davidson, Sean Ryan Fanello, Adarsh Kowdle, Sergio Orts-Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, Pushmeet Kohli, Vladimir Tankovich, Shahram Izadi:
Fusion4D: real-time performance capture of challenging scenes.