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2020 – today
- 2022
[i36]Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga, Dan Richter Jørgensen, Ole Winther, Alfiia Galimzianova:
DermX: an end-to-end framework for explainable automated dermatological diagnosis. CoRR abs/2202.06956 (2022)- [i35]Darius Chira, Ilian Haralampiev, Ole Winther, Andrea Dittadi, Valentin Liévin:
Image Super-Resolution With Deep Variational Autoencoders. CoRR abs/2203.09445 (2022) - [i34]Samuele Papa, Ole Winther, Andrea Dittadi:
Inductive Biases for Object-Centric Representations in the Presence of Complex Textures. CoRR abs/2204.08479 (2022) - [i33]Giorgio Giannone, Didrik Nielsen, Ole Winther:
Few-Shot Diffusion Models. CoRR abs/2205.15463 (2022) - 2021
- [c49]Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf:
On the Transfer of Disentangled Representations in Realistic Settings. ICLR 2021 - [i32]Andrea Dittadi, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello:
Generalization and Robustness Implications in Object-Centric Learning. CoRR abs/2107.00637 (2021) - [i31]Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter V. Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. CoRR abs/2107.05686 (2021) - [i30]Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, Tejs Vegge:
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks. CoRR abs/2107.06068 (2021) - [i29]Giorgio Giannone, Ole Winther:
Hierarchical Few-Shot Generative Models. CoRR abs/2110.12279 (2021) - 2020
- [j35]Christopher Heje Grønbech
, Maximillian Fornitz Vording, Pascal N. Timshel, Casper Kaae Sønderby, Tune H. Pers, Ole Winther, Bonnie Berger:
scVAE: variational auto-encoders for single-cell gene expression data. Bioinform. 36(16): 4415-4422 (2020) - [c48]Valentin Liévin, Andrea Dittadi, Anders Christensen, Ole Winther:
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds. NeurIPS 2020 - [c47]Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling:
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. NeurIPS 2020 - [c46]Didrik Nielsen, Ole Winther:
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. NeurIPS 2020 - [i28]Didrik Nielsen, Ole Winther:
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. CoRR abs/2002.02547 (2020) - [i27]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) - [i26]Valentin Liévin, Andrea Dittadi, Anders Christensen, Ole Winther:
Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds. CoRR abs/2008.01998 (2020) - [i25]Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf:
On the Transfer of Disentangled Representations in Realistic Settings. CoRR abs/2010.14407 (2020)
2010 – 2019
- 2019
- [j34]Savvas Kinalis, Finn Cilius Nielsen, Ole Winther, Frederik Otzen Bagger:
Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data. BMC Bioinform. 20(1): 379:1-379:9 (2019) - [c45]Rasmus Berg Palm, Florian Laws, Ole Winther:
Attend, Copy, Parse End-to-end Information Extraction from Documents. ICDAR 2019: 329-336 - [c44]Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther:
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling. NeurIPS 2019: 6548-6558 - [i24]Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther:
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling. CoRR abs/1902.02102 (2019) - [i23]Andrea Dittadi, Ole Winther:
LAVAE: Disentangling Location and Appearance. CoRR abs/1909.11813 (2019) - 2018
- [c43]Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo:
Bayesian Structure Learning for Dynamic Brain Connectivity. AISTATS 2018: 1436-1446 - [c42]Andrea Dittadi, Thomas Bolander, Ole Winther:
Learning to Plan from Raw Data in Grid-based Games. GCAI 2018: 54-67 - [c41]Rasmus Berg Palm, Ulrich Paquet, Ole Winther:
Recurrent Relational Networks. NeurIPS 2018: 3372-3382 - [i22]Rasmus Berg Palm, Florian Laws, Ole Winther:
Attend, Copy, Parse - End-to-end information extraction from documents. CoRR abs/1812.07248 (2018) - 2017
- [j33]José Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther:
DeepLoc: prediction of protein subcellular localization using deep learning. Bioinform. 33(21): 3387-3395 (2017) - [j32]Vanessa Isabell Jurtz, Alexander Rosenberg Johansen, Morten Nielsen, José Juan Almagro Armenteros, Henrik Nielsen, Casper Kaae Sønderby, Ole Winther, Søren Kaae Sønderby:
An introduction to deep learning on biological sequence data: examples and solutions. Bioinform. 33(22): 3685-3690 (2017) - [j31]José Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther:
DeepLoc: prediction of protein subcellular localization using deep learning. Bioinform. 33(24): 4049 (2017) - [j30]Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen:
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. J. Mach. Learn. Res. 18: 139:1-139:58 (2017) - [j29]Ditte Høvenhoff Hald, Ricardo Henao, Ole Winther:
Gaussian process based independent analysis for temporal source separation in fMRI. NeuroImage 152: 563-574 (2017) - [c40]Alexander Rosenberg Johansen, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther:
Deep Recurrent Conditional Random Field Network for Protein Secondary Prediction. BCB 2017: 73-78 - [c39]Rasmus Berg Palm, Dirk Hovy, Florian Laws, Ole Winther:
End-to-End Information Extraction without Token-Level Supervision. SCNLP@EMNLP 2017 2017: 48-52 - [c38]Rasmus Berg Palm, Ole Winther, Florian Laws:
CloudScan - A Configuration-Free Invoice Analysis System Using Recurrent Neural Networks. ICDAR 2017: 406-413 - [c37]Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury:
Dynamical functional theory for compressed sensing. ISIT 2017: 2143-2147 - [c36]Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther:
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. NIPS 2017: 3601-3610 - [c35]Dan Svenstrup, Jonas Meinertz Hansen, Ole Winther:
Hash Embeddings for Efficient Word Representations. NIPS 2017: 4928-4936 - [i21]Lars Maaløe, Marco Fraccaro, Ole Winther:
Semi-Supervised Generation with Cluster-aware Generative Models. CoRR abs/1704.00637 (2017) - [i20]Burak Çakmak, Manfred Opper, Ole Winther, Bernard H. Fleury:
Dynamical Functional Theory for Compressed Sensing. CoRR abs/1705.04284 (2017) - [i19]Rasmus Berg Palm, Dirk Hovy, Florian Laws, Ole Winther:
End-to-End Information Extraction without Token-Level Supervision. CoRR abs/1707.04913 (2017) - [i18]Rasmus Berg Palm, Ole Winther, Florian Laws:
CloudScan - A configuration-free invoice analysis system using recurrent neural networks. CoRR abs/1708.07403 (2017) - [i17]Dan Svenstrup, Jonas Meinertz Hansen, Ole Winther:
Hash Embeddings for Efficient Word Representations. CoRR abs/1709.03933 (2017) - [i16]Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther:
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. CoRR abs/1710.05741 (2017) - [i15]Rasmus Berg Palm, Ulrich Paquet, Ole Winther:
Recurrent Relational Networks for Complex Relational Reasoning. CoRR abs/1711.08028 (2017) - 2016
- [j28]Aki Vehtari, Tommi Mononen, Ville Tolvanen, Tuomas Sivula, Ole Winther:
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models. J. Mach. Learn. Res. 17: 103:1-103:38 (2016) - [j27]Frederik Otzen Bagger, Damir Sasivarevic, Sina Hadi Sohi, Linea Gøricke Laursen, Sachin Pundhir, Casper Kaae Sønderby, Ole Winther, Nicolas Rapin, Bo T. Porse:
BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis. Nucleic Acids Res. 44(Database-Issue): 917-924 (2016) - [c34]Marco Fraccaro, Ulrich Paquet, Ole Winther:
Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search. AAAI 2016: 1554-1560 - [c33]Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg:
Bayesian Generalised Ensemble Markov Chain Monte Carlo. AISTATS 2016: 408-416 - [c32]Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther:
Auxiliary Deep Generative Models. ICML 2016: 1445-1453 - [c31]Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther:
Autoencoding beyond pixels using a learned similarity metric. ICML 2016: 1558-1566 - [c30]Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther:
Sequential Neural Models with Stochastic Layers. NIPS 2016: 2199-2207 - [c29]Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther:
Ladder Variational Autoencoders. NIPS 2016: 3738-3746 - [i14]Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther:
How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks. CoRR abs/1602.02282 (2016) - [i13]Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther:
Auxiliary Deep Generative Models. CoRR abs/1602.05473 (2016) - [i12]Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther:
Sequential Neural Models with Stochastic Layers. CoRR abs/1605.07571 (2016) - [i11]Burak Çakmak, Manfred Opper, Bernard H. Fleury, Ole Winther:
Self-Averaging Expectation Propagation. CoRR abs/1608.06602 (2016) - [i10]Alexander Rosenberg Johansen, Jonas Meinertz Hansen, Elias Khazen Obeid, Casper Kaae Sønderby, Ole Winther:
Neural Machine Translation with Characters and Hierarchical Encoding. CoRR abs/1610.06550 (2016) - 2015
- [c28]Søren Kaae Sønderby, Casper Kaae Sønderby, Henrik Nielsen, Ole Winther:
Convolutional LSTM Networks for Subcellular Localization of Proteins. AlCoB 2015: 68-80 - [c27]Burak Çakmak, Ole Winther, Bernard H. Fleury:
S-AMP for non-linear observation models. ISIT 2015: 2807-2811 - [i9]Lars Maaloe, Morten Arngren, Ole Winther:
Deep Belief Nets for Topic Modeling. CoRR abs/1501.04325 (2015) - [i8]Burak Çakmak, Ole Winther, Bernard H. Fleury:
S-AMP for Non-linear Observation Models. CoRR abs/1501.06216 (2015) - [i7]Søren Kaae Sønderby, Casper Kaae Sønderby, Henrik Nielsen, Ole Winther:
Convolutional LSTM Networks for Subcellular Localization of Proteins. CoRR abs/1503.01919 (2015) - [i6]Manfred Opper, Burak Çakmak, Ole Winther:
A Theory of Solving TAP Equations for Ising Models with General Invariant Random Matrices. CoRR abs/1509.01229 (2015) - [i5]Søren Kaae Sønderby, Casper Kaae Sønderby, Lars Maaløe, Ole Winther:
Recurrent Spatial Transformer Networks. CoRR abs/1509.05329 (2015) - [i4]Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Ole Winther:
Autoencoding beyond pixels using a learned similarity metric. CoRR abs/1512.09300 (2015) - 2014
- [j26]Niels H. Christiansen, Per Erlend Torbergsen Voie, Ole Winther, Jan Høgsberg:
Comparison of Neural Network Error Measures for Simulation of Slender Marine Structures. J. Appl. Math. 2014: 759834:1-759834:11 (2014) - [c26]David Kofoed Wind, Ole Winther:
Model Selection in Data Analysis Competitions. MetaSel@ECAI 2014: 55-60 - [c25]Darko Zibar, Ole Winther, Robert Borkowski, Idelfonso Tafur Monroy, Luis Henrique Hecker de Carvalho, Júlio Oliveira:
Applications of expectation maximization algorithm for coherent optical communication. EUSIPCO 2014: 1890-1894 - [c24]Burak Çakmak, Ole Winther, Bernard H. Fleury:
S-AMP: Approximate message passing for general matrix ensembles. ITW 2014: 192-196 - [c23]Michael Riis Andersen, Ole Winther, Lars Kai Hansen:
Bayesian Inference for Structured Spike and Slab Priors. NIPS 2014: 1745-1753 - [i3]Burak Çakmak, Ole Winther, Bernard H. Fleury:
S-AMP: Approximate Message Passing for General Matrix Ensembles. CoRR abs/1405.2767 (2014) - [i2]Søren Kaae Sønderby, Ole Winther:
Protein Secondary Structure Prediction with Long Short Term Memory Networks. CoRR abs/1412.7828 (2014) - 2013
- [j25]Radu Dragusin, Paula Petcu, Christina Lioma, Birger Larsen, Henrik Jørgensen, Ingemar J. Cox, Lars Kai Hansen, Peter Ingwersen, Ole Winther:
FindZebra: A search engine for rare diseases. Int. J. Medical Informatics 82(6): 528-538 (2013) - [j24]Manfred Opper, Ulrich Paquet, Ole Winther:
Perturbative corrections for approximate inference in Gaussian latent variable models. J. Mach. Learn. Res. 14(1): 2857-2898 (2013) - [j23]Frederik Otzen Bagger, Nicolas Rapin, Kim Theilgaard-Mönch, Bogumil Kaczkowski, Lina A. Thoren, Johan Jendholm, Ole Winther, Bo T. Porse:
HemaExplorer: a database of mRNA expression profiles in normal and malignant haematopoiesis. Nucleic Acids Res. 41(Database-Issue): 1034-1039 (2013) - [i1]Radu Dragusin, Paula Petcu, Christina Lioma, Birger Larsen, Henrik Jørgensen, Ingemar J. Cox, Lars Kai Hansen, Peter Ingwersen, Ole Winther:
FindZebra: A search engine for rare diseases. CoRR abs/1303.3229 (2013) - 2012
- [j22]Ricardo Henao, Ole Winther:
Predictive active set selection methods for Gaussian processes. Neurocomputing 80: 10-18 (2012) - [j21]Ulrich Paquet, Blaise Thomson, Ole Winther:
A hierarchical model for ordinal matrix factorization. Stat. Comput. 22(4): 945-957 (2012) - 2011
- [j20]Ricardo Henao, Ole Winther:
Sparse Linear Identifiable Multivariate Modeling. J. Mach. Learn. Res. 12: 863-905 (2011) - [j19]Carsten Stahlhut, Morten Mørup, Ole Winther, Lars Kai Hansen:
Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE) Using a Hierarchical Bayesian Approach. J. Signal Process. Syst. 65(3): 431-444 (2011) - [c22]Radu Dragusin, Paula Petcu, Christina Lioma, Birger Larsen, Henrik Jørgensen, Ole Winther:
Rare Disease Diagnosis as an Information Retrieval Task. ICTIR 2011: 356-359 - 2010
- [j18]Lisbeth Carstensen, Albin Sandelin, Ole Winther, Niels Richard Hansen:
Multivariate Hawkes process models of the occurrence of regulatory elements. BMC Bioinform. 11: 456 (2010) - [j17]Morten Hansen, Lars P. B. Christensen, Ole Winther:
Computing the minimum-phase filter using the QL-factorization. IEEE Trans. Signal Process. 58(6): 3195-3205 (2010)
2000 – 2009
- 2009
- [j16]Ulrich Paquet, Ole Winther, Manfred Opper:
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications. J. Mach. Learn. Res. 10: 1263-1304 (2009) - [j15]Eivind Valen, Albin Sandelin, Ole Winther, Anders Krogh:
Discovery of Regulatory Elements is Improved by a Discriminatory Approach. PLoS Comput. Biol. 5(11) (2009) - [c21]Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen:
Bayesian Non-negative Matrix Factorization. ICA 2009: 540-547 - [c20]Carsten Stahlhut, Morten Mørup, Ole Winther, Lars Kai Hansen:
Sofomore: Combined EEG Source and Forward Model Reconstruction. ISBI 2009: 450-453 - [c19]Ricardo Henao, Ole Winther:
Bayesian Sparse Factor Models and DAGs Inference and Comparison. NIPS 2009: 736-744 - 2008
- [j14]Man-Hung Eric Tang, Anders Krogh, Ole Winther:
BayesMD: Flexible Biological Modeling for Motif Discovery. J. Comput. Biol. 15(10): 1347-1363 (2008) - [j13]Jan Christian Bryne, Eivind Valen, Man-Hung Eric Tang, Troels Torben Marstrand, Ole Winther, Isabelle da Piedade, Anders Krogh, Boris Lenhard, Albin Sandelin:
JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update. Nucleic Acids Res. 36(Database-Issue): 102-106 (2008) - [c18]Manfred Opper, Ulrich Paquet, Ole Winther:
Improving on Expectation Propagation. NIPS 2008: 1241-1248 - 2007
- [j12]Ole Winther, Kaare Brandt Petersen:
Bayesian independent component analysis: Variational methods and non-negative decompositions. Digit. Signal Process. 17(5): 858-872 (2007) - [j11]Ole Winther, Kaare Brandt Petersen:
Flexible and efficient implementations of Bayesian independent component analysis. Neurocomputing 71(1-3): 221-233 (2007) - [j10]Thomas Beierholm, Ole Winther:
Particle Filter Inference in an Articulatory-Based Speech Model. IEEE Signal Process. Lett. 14(11): 883-886 (2007) - [c17]Morten Hansen, Ole Winther, Lars P. B. Christensen:
On Sphere Detection and Minimum-Phase Prefiltered Reduced-State Sequence Estimation. GLOBECOM 2007: 4237-4241 - [c16]Fei Wang, Shijun Wang, Changshui Zhang, Ole Winther:
Semi-Supervised Mean Fields. AISTATS 2007: 596-603 - 2006
- [j9]Thomas Grotkjær, Ole Winther, Birgitte Regenberg, Jens Nielsen, Lars Kai Hansen:
Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm. Bioinform. 22(1): 58-67 (2006) - 2005
- [j8]Manfred Opper, Ole Winther:
Expectation Consistent Approximate Inference. J. Mach. Learn. Res. 6: 2177-2204 (2005) - [j7]Kaare Brandt Petersen, Ole Winther, Lars Kai Hansen:
On the Slow Convergence of EM and VBEM in Low-Noise Linear Models. Neural Comput. 17(9): 1921-1926 (2005) - [c15]Kaare Brandt Petersen, Ole Winther:
The EM algorithm in independent component analysis. ICASSP (5) 2005: 169-172 - 2004
- [c14]Manfred Opper, Ole Winther:
Approximate Inference in Probabilistic Models. ALT 2004: 494-504 - [c13]Thomas Beierholm, Brian Dam Pedersen, Ole Winther:
Low complexity Bayesian single channel source separation. ICASSP (5) 2004: 529-532 - [c12]Manfred Opper, Ole Winther:
Expectation Consistent Free Energies for Approximate Inference. NIPS 2004: 1001-1008 - 2003
- [j6]Lehel Csató, Manfred Opper, Ole Winther:
Tractable inference for probabilistic data models. Complex. 8(4): 64-68 (2003) - [c11]Manfred Opper, Ole Winther:
Variational Linear Response. NIPS 2003: 1157-1164 - 2002
- [j5]Pedro A. d. F. R. Højen-Sørensen, Ole Winther, Lars Kai Hansen:
Analysis of functional neuroimages using ICA with adaptive binary sources. Neurocomputing 49(1-4): 213-225 (2002) - [j4]Pedro A. d. F. R. Højen-Sørensen, Ole Winther, Lars Kai Hansen:
Mean-Field Approaches to Independent Component Analysis. Neural Comput. 14(4): 889-918 (2002) - [c10]Joaquin Quiñonero Candela, Ole Winther:
Incremental Gaussian Processes. NIPS 2002: 1001-1008 - [c9]Thomas Kolenda, Lars Kai Hansen, Jan Larsen, Ole Winther:
Independent component analysis for understanding multimedia content. NNSP 2002: 757-766 - 2001
- [c8]Lehel Csató, Manfred Opper, Ole Winther:
TAP Gibbs Free Energy, Belief Propagation and Sparsity. NIPS 2001: 657-663 - 2000
- [j3]Manfred Opper, Ole Winther:
Gaussian Processes for Classification: Mean-Field Algorithms. Neural Comput. 12(11): 2655-2684 (2000) - [c7]Ole Winther:
Computing with Finite and Infinite Networks. NIPS 2000: 336-342 - [c6]Pedro A. d. F. R. Højen-Sørensen, Ole Winther, Lars Kai Hansen:
Ensemble Learning and Linear Response Theory for ICA. NIPS 2000: 542-548
1990 – 1999
- 1999
- [c5]Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther:
Efficient Approaches to Gaussian Process Classification. NIPS 1999: 251-257 - 1998
- [c4]Manfred Opper, Ole Winther:
Mean Field Methods for Classification with Gaussian Processes. NIPS 1998: 309-315 - 1997
- [c3]Ole Winther, Sara A. Solla:
Bayesian online learning in the perceptron. ESANN 1997 - 1996
- [c2]Søren Halkjær, Ole Winther:
The Effect of Correlated Input Data on the Dynamics of Learning. NIPS 1996: 169-175 - [c1]Manfred Opper, Ole Winther:
A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks. NIPS 1996: 225-231 - 1993
- [j2]Jan Gorodkin, A. Sørensen, Ole Winther:
Neural Networks and Cellular Automata Complexity. Complex Syst. 7(1) (1993) - [j1]Jan Gorodkin, Lars Kai Hansen, Anders Krogh, Claus Svarer, Ole Winther:
A Quantitative Study Of Pruning By Optimal Brain Damage. Int. J. Neural Syst. 4(2): 159-169 (1993)