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ORCIDGal Chechik
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- affiliation: Stanford University, USA
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2020 – today
- 2021
[c57]Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya:
Auxiliary Learning by Implicit Differentiation. ICLR 2021- [c56]Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik:
Learning the Pareto Front with Hypernetworks. ICLR 2021 - [c55]Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya:
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. ICML 2021: 54-65 - [c54]Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson:
Compositional Video Synthesis with Action Graphs. ICML 2021: 662-673 - [c53]Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik:
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. ICML 2021: 7565-7577 - [c52]Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik:
Personalized Federated Learning using Hypernetworks. ICML 2021: 9489-9502 - [c51]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
From Local Structures to Size Generalization in Graph Neural Networks. ICML 2021: 11975-11986 - [c50]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements (Extended Abstract). IJCAI 2021: 4794-4798 - [c49]Idan Achituve, Haggai Maron, Gal Chechik:
Self-Supervised Learning for Domain Adaptation on Point Clouds. WACV 2021: 123-133 - [c48]Dvir Samuel, Yuval Atzmon, Gal Chechik:
From generalized zero-shot learning to long-tail with class descriptors. WACV 2021: 286-295 - [i43]Sangho Lee, Jiwan Chung, Youngjae Yu, Gunhee Kim, Thomas M. Breuel, Gal Chechik, Yale Song:
Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning. CoRR abs/2101.10803 (2021) - [i42]Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya:
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. CoRR abs/2102.07868 (2021) - [i41]Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor:
Reinforcement Learning for Datacenter Congestion Control. CoRR abs/2102.09337 (2021) - [i40]Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik:
Personalized Federated Learning using Hypernetworks. CoRR abs/2103.04628 (2021) - [i39]Dvir Samuel, Gal Chechik:
Distributional Robustness Loss for Long-tail Learning. CoRR abs/2104.03066 (2021) - [i38]Amir Bar, Xin Wang, Vadim Kantorov, Colorado J. Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson:
DETReg: Unsupervised Pretraining with Region Priors for Object Detection. CoRR abs/2106.04550 (2021) - [i37]Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya:
Personalized Federated Learning with Gaussian Processes. CoRR abs/2106.15482 (2021) - [i36]Assaf Hallak, Gal Dalal, Steven Dalton, Iuri Frosio, Shie Mannor, Gal Chechik:
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction. CoRR abs/2107.01715 (2021) - [i35]Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, Daniel Cohen-Or:
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators. CoRR abs/2108.00946 (2021) - [i34]Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit:
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. CoRR abs/2110.06539 (2021) - [i33]Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson:
Object-Region Video Transformers. CoRR abs/2110.06915 (2021) - [i32]Ohad Amosy, Gal Eyal, Gal Chechik:
Inference-Time Personalized Federated Learning. CoRR abs/2111.08356 (2021) - 2020
- [c47]Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, Gal Chechik:
Learning Object Permanence from Video. ECCV (16) 2020: 35-50 - [c46]Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson:
Learning Canonical Representations for Scene Graph to Image Generation. ECCV (26) 2020: 210-227 - [c45]Tanmay Gupta, Arash Vahdat, Gal Chechik, Xiaodong Yang, Jan Kautz, Derek Hoiem:
Contrastive Learning for Weakly Supervised Phrase Grounding. ECCV (3) 2020: 752-768 - [c44]Tzuf Paz-Argaman, Reut Tsarfaty, Gal Chechik, Yuval Atzmon:
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization. EMNLP (Findings) 2020: 569-579 - [c43]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements. ICML 2020: 6734-6744 - [c42]Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik:
A causal view of compositional zero-shot recognition. NeurIPS 2020 - [c41]Moshiko Raboh, Roei Herzig, Jonathan Berant, Gal Chechik, Amir Globerson:
Differentiable Scene Graphs. WACV 2020: 1477-1486 - [i31]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements. CoRR abs/2002.08599 (2020) - [i30]Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, Gal Chechik:
Learning Object Permanence from Video. CoRR abs/2003.10469 (2020) - [i29]Idan Achituve, Haggai Maron, Gal Chechik:
Self-Supervised Learning for Domain Adaptation on Point-Clouds. CoRR abs/2003.12641 (2020) - [i28]Dvir Samuel, Yuval Atzmon, Gal Chechik:
Long-tail learning with attributes. CoRR abs/2004.02235 (2020) - [i27]Tanmay Gupta, Arash Vahdat, Gal Chechik, Xiaodong Yang, Jan Kautz, Derek Hoiem:
Contrastive Learning for Weakly Supervised Phrase Grounding. CoRR abs/2006.09920 (2020) - [i26]Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik:
A causal view of compositional zero-shot recognition. CoRR abs/2006.14610 (2020) - [i25]Amir Bar, Roei Herzig, Xiaolong Wang, Gal Chechik, Trevor Darrell, Amir Globerson:
Compositional Video Synthesis with Action Graphs. CoRR abs/2006.15327 (2020) - [i24]Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya:
Auxiliary Learning by Implicit Differentiation. CoRR abs/2007.02693 (2020) - [i23]Achiya Jerbi, Roei Herzig, Jonathan Berant, Gal Chechik, Amir Globerson:
Learning Object Detection from Captions via Textual Scene Attributes. CoRR abs/2009.14558 (2020) - [i22]Tzuf Paz-Argaman, Yuval Atzmon, Gal Chechik, Reut Tsarfaty:
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization. CoRR abs/2010.03276 (2020) - [i21]Aviv Navon, Aviv Shamsian, Gal Chechik, Ethan Fetaya:
Learning the Pareto Front with Hypernetworks. CoRR abs/2010.04104 (2020) - [i20]Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik:
How to Stop Epidemics: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. CoRR abs/2010.05313 (2020) - [i19]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
On Size Generalization in Graph Neural Networks. CoRR abs/2010.08853 (2020) - [i18]Ohad Amosy, Gal Chechik:
Teacher-Student Consistency For Multi-Source Domain Adaptation. CoRR abs/2010.10054 (2020)
2010 – 2019
- 2019
- [c40]Roman Visotsky, Yuval Atzmon, Gal Chechik
:
Learning with Per-Sample Side Information. AGI 2019: 209-219 - [c39]Yuval Atzmon, Gal Chechik:
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning. CVPR 2019: 11671-11680 - [c38]Lior Bracha, Gal Chechik:
Informative Object Annotations: Tell Me Something I Don't Know. CVPR 2019: 12507-12515 - [c37]Hagai Taitelbaum, Gal Chechik, Jacob Goldberger:
Multilingual word translation using auxiliary languages. EMNLP/IJCNLP (1) 2019: 1330-1335 - [c36]Hagai Taitelbaum, Gal Chechik, Jacob Goldberger:
A Multi-Pairwise Extension of Procrustes Analysis for Multilingual Word Translation. EMNLP/IJCNLP (1) 2019: 3558-3563 - [c35]Hagai Taitelbaum, Gal Chechik, Jacob Goldberger:
Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones. ICASSP 2019: 3637-3641 - [c34]Gilad Vered, Gal Oren, Yuval Atzmon, Gal Chechik:
Joint Optimization for Cooperative Image Captioning. ICCV 2019: 8897-8906 - [c33]Renana Opochinsky, Bracha Laufer-Goldshtein, Sharon Gannot, Gal Chechik:
Deep Ranking-Based Sound Source Localization. WASPAA 2019: 283-287 - [i17]Moshiko Raboh, Roei Herzig, Gal Chechik, Jonathan Berant, Amir Globerson:
Learning Latent Scene-Graph Representations for Referring Relationships. CoRR abs/1902.10200 (2019) - [i16]Roman Visotsky, Yuval Atzmon, Gal Chechik:
Few-Shot Learning with Per-Sample Rich Supervision. CoRR abs/1906.03859 (2019) - [i15]Gilad Vered, Gal Oren, Yuval Atzmon, Gal Chechik:
Cooperative image captioning. CoRR abs/1907.11565 (2019) - [i14]Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson:
Learning Canonical Representations for Scene Graph to Image Generation. CoRR abs/1912.07414 (2019) - 2018
- [c32]Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson:
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction. NeurIPS 2018: 7211-7221 - [c31]Yuval Atzmon, Gal Chechik:
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning. UAI 2018: 382-392 - [i13]Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson:
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction. CoRR abs/1802.05451 (2018) - [i12]Yuval Atzmon, Gal Chechik:
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning. CoRR abs/1806.02664 (2018) - [i11]Yair Lakretz, Gal Chechik, Evan-Gary Cohen, Alessandro Treves, Naama Friedmann:
Metric Learning for Phoneme Perception. CoRR abs/1809.07824 (2018) - [i10]Yuval Atzmon, Gal Chechik:
Domain-Aware Generalized Zero-Shot Learning. CoRR abs/1812.09903 (2018) - [i9]Lior Bracha, Gal Chechik:
Informative Object Annotations: Tell Me Something I Don't Know. CoRR abs/1812.10358 (2018) - 2017
- [j24]Alon Zweig, Gal Chechik:
Group online adaptive learning. Mach. Learn. 106(9-10): 1747-1770 (2017) - [c30]Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik:
Context-Aware Captions from Context-Agnostic Supervision. CVPR 2017: 1070-1079 - [c29]Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, Serge J. Belongie:
Learning from Noisy Large-Scale Datasets with Minimal Supervision. CVPR 2017: 6575-6583 - [c28]Ido Cohen, Eli (Omid) David, Nathan S. Netanyahu, Noa Liscovitch, Gal Chechik:
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders. ICANN (2) 2017: 287-296 - [c27]Qifan Wang, Gal Chechik, Chen Sun, Bin Shen:
Instance-Level Label Propagation with Multi-Instance Learning. IJCAI 2017: 2943-2949 - [i8]Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, Serge J. Belongie:
Learning From Noisy Large-Scale Datasets With Minimal Supervision. CoRR abs/1701.01619 (2017) - [i7]Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik:
Context-aware Captions from Context-agnostic Supervision. CoRR abs/1701.02870 (2017) - [i6]Ido Cohen, Eli David, Nathan S. Netanyahu, Noa Liscovitch, Gal Chechik:
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders. CoRR abs/1711.09663 (2017) - 2016
- [j23]Lior Kirsch, Gal Chechik:
On Expression Patterns and Developmental Origin of Human Brain Regions. PLoS Comput. Biol. 12(8) (2016) - [i5]Yuval Atzmon, Jonathan Berant, Vahid Kezami, Amir Globerson, Gal Chechik:
Learning to generalize to new compositions in image understanding. CoRR abs/1608.07639 (2016) - 2015
- [j22]Ossnat Bar-Shira, Ronnie Maor, Gal Chechik:
Gene Expression Switching of Receptor Subunits in Human Brain Development. PLoS Comput. Biol. 11(12) (2015) - [c26]Yair Lakretz, Gal Chechik, Naama Friedmann, Michal Rosen-Zvi:
Probabilistic Graphical Models of Dyslexia. KDD 2015: 1919-1928 - [c25]Yuval Atzmon, Uri Shalit, Gal Chechik:
Learning Sparse Metrics, One Feature at a Time. FE@NIPS 2015: 30-48 - [c24]Alexander Kalmanovich, Gal Chechik:
Gradual Training Method for Denoising Auto Encoders. ICLR (Workshop) 2015 - 2014
- [j21]Grégoire Mesnil, Antoine Bordes, Jason Weston, Gal Chechik, Yoshua Bengio:
Learning semantic representations of objects and their parts. Mach. Learn. 94(2): 281-301 (2014) - [c23]Uri Shalit, Gal Chechik:
Coordinate-descent for learning orthogonal matrices through Givens rotations. ICML 2014: 548-556 - [i4]Alexander Kalmanovich, Gal Chechik:
Gradual training of deep denoising auto encoders. CoRR abs/1412.6257 (2014) - 2013
- [j20]Noa Liscovitch, Uri Shalit, Gal Chechik:
FuncISH: learning a functional representation of neural ISH images. Bioinform. 29(13): 36-43 (2013) - [j19]Noa Liscovitch, Gal Chechik:
Specialization of Gene Expression during Mouse Brain Development. PLoS Comput. Biol. 9(9) (2013) - [c22]Uri Shalit, Daphna Weinshall, Gal Chechik:
Modeling Musical Influence with Topic Models. ICML (2) 2013: 244-252 - [i3]Uri Shalit, Gal Chechik:
Efficient coordinate-descent for orthogonal matrices through Givens rotations. CoRR abs/1312.0624 (2013) - 2012
- [j18]Uri Shalit, Daphna Weinshall, Gal Chechik:
Online Learning in the Embedded Manifold of Low-rank Matrices. J. Mach. Learn. Res. 13: 429-458 (2012) - [j17]Lior Kirsch, Noa Liscovitch, Gal Chechik:
Localizing Genes to Cerebellar Layers by Classifying ISH Images. PLoS Comput. Biol. 8(12) (2012) - [c21]Koby Crammer, Gal Chechik:
Adaptive Regularization for Similarity Measures. ICML 2012 - [i2]Koby Crammer, Gal Chechik:
Adaptive Regularization for Weight Matrices. CoRR abs/1206.4639 (2012) - [i1]Amir Globerson, Gal Chechik, Naftali Tishby:
Sufficient Dimensionality Reduction with Irrelevant Statistics. CoRR abs/1212.2483 (2012) - 2011
- [c20]Richard F. Lyon, Jay Ponte, Gal Chechik:
Sparse coding of auditory features for machine hearing in interference. ICASSP 2011: 5876-5879 - 2010
- [j16]Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio:
Large Scale Online Learning of Image Similarity Through Ranking. J. Mach. Learn. Res. 11: 1109-1135 (2010) - [j15]Richard F. Lyon, Martin Rehn, Samy Bengio, Thomas C. Walters, Gal Chechik:
Sound Retrieval and Ranking Using Sparse Auditory Representations. Neural Comput. 22(9): 2390-2416 (2010) - [c19]Geremy Heitz, Gal Chechik:
Object separation in x-ray image sets. CVPR 2010: 2093-2100 - [c18]Uri Shalit, Daphna Weinshall, Gal Chechik:
Online Learning in The Manifold of Low-Rank Matrices. NIPS 2010: 2128-2136
2000 – 2009
- 2009
- [j14]Gal Chechik, Daphne Koller:
Timing of Gene Expression Responses to Environmental Changes. J. Comput. Biol. 16(2): 279-290 (2009) - [c17]Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio:
Large Scale Online Learning of Image Similarity through Ranking. IbPRIA 2009: 11-14 - [c16]Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio:
An Online Algorithm for Large Scale Image Similarity Learning. NIPS 2009: 306-314 - 2008
- [j13]Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
Max-margin Classification of Data with Absent Features. J. Mach. Learn. Res. 9: 1-21 (2008) - [c15]Gal Chechik, Eugene Ie, Martin Rehn, Samy Bengio, Dick Lyon:
Large-scale content-based audio retrieval from text queries. Multimedia Information Retrieval 2008: 105-112 - 2007
- [j12]Gal Chechik, Christina S. Leslie, William Stafford Noble, Gunnar Rätsch, Quaid Morris, Koji Tsuda:
NIPS workshop on New Problems and Methods in Computational Biology. BMC Bioinform. 8(S-10) (2007) - [j11]Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby:
Euclidean Embedding of Co-occurrence Data. J. Mach. Learn. Res. 8: 2265-2295 (2007) - 2006
- [j10]Sean O'Rourke, Gal Chechik, Robin Friedman, Eleazar Eskin:
Discrete profile comparison using information bottleneck. BMC Bioinform. 7(S-1) (2006) - [c14]Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby:
Embedding Heterogeneous Data Using Statistical Models. AAAI 2006: 1605-1608 - [c13]Alexis Battle, Gal Chechik, Daphne Koller:
Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks. NIPS 2006: 121-128 - [c12]Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
Max-margin classification of incomplete data. NIPS 2006: 233-240 - 2005
- [j9]Israel Nelken, Gal Chechik, Thomas D. Mrsic-Flogel, Andrew J. King, Jan W. H. Schnupp:
Encoding Stimulus Information by Spike Numbers and Mean Response Time in Primary Auditory Cortex. J. Comput. Neurosci. 19(2): 199-221 (2005) - [j8]Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss:
Information Bottleneck for Gaussian Variables. J. Mach. Learn. Res. 6: 165-188 (2005) - 2004
- [c11]Koby Crammer, Gal Chechik:
A needle in a haystack: local one-class optimization. ICML 2004 - [c10]Amir Globerson, Gal Chechik, Fernando C. N. Pereira, Naftali Tishby:
Euclidean Embedding of Co-Occurrence Data. NIPS 2004: 497-504 - [c9]Sean O'Rourke, Gal Chechik, Robin Friedman, Eleazar Eskin:
Discrete profile alignment via constrained information bottleneck. NIPS 2004: 1009-1016 - 2003
- [j7]Gal Chechik:
Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization. Neural Comput. 15(7): 1481-1510 (2003) - [c8]Hezi Avraham, Gal Chechik, Eytan Ruppin:
Are There Representations in Embodied Evolved Agents? Taking Measures. ECAL 2003: 743-752 - [c7]Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss:
Information Bottleneck for Gaussian Variables. NIPS 2003: 1213-1220 - [c6]Amir Globerson, Gal Chechik, Naftali Tishby:
Sufficient Dimensionality Reduction with Irrelevance Statistics. UAI 2003: 281-288 - 2002
- [c5]Gal Chechik, Naftali Tishby:
Extracting Relevant Structures with Side Information. NIPS 2002: 857-864 - 2001
- [j6]Gal Chechik:
Spike timing dependent plasticity and mutual information in spiking neurons. Neurocomputing 38-40: 147-152 (2001) - [j5]Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Effective Neuronal Learning with Ineffective Hebbian Learning Rules. Neural Comput. 13(4): 817-840 (2001) - [c4]Gal Chechik, Amir Globerson, Michael J. Anderson, Eric D. Young, Israel Nelken, Naftali Tishby:
Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway. NIPS 2001: 173-180 - 2000
- [j4]Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Neuronal normalization provides effective learning through ineffective synaptic learning rules. Neurocomputing 32-33: 345-351 (2000) - [c3]Gal Chechik, Naftali Tishby:
Temporally Dependent Plasticity: An Information Theoretic Account. NIPS 2000: 110-116
1990 – 1999
- 1999
- [j3]Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Neuronal regulation: A biologically plausible mechanism for efficient synaptic pruning in development. Neurocomputing 26-27: 633-639 (1999) - [j2]Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Neuronal Regulation: A Mechanism for Synaptic Pruning During Brain Maturation. Neural Comput. 11(8): 2061-2080 (1999) - [c2]Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Effective Learning Requires Neuronal Remodeling of Hebbian Synapses. NIPS 1999: 96-102 - 1998
- [j1]Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Synaptic Pruning In Development: A Computational Account. Neural Comput. 10(7): 1759-1777 (1998) - [c1]Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Neuronal Regulation Implements Efficient Synaptic Pruning. NIPS 1998: 97-103
Coauthor Index