Haim Sompolinsky

61 papers A* 6Journal 41Unranked 13

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Closing the Data-Efficiency Gap Between Autoregressive and Masked Diffusion LLMs. CoRR Xu Pan, Ely Hahami, Jingxuan Fan, Ziqian Xie, Haim Sompolinsky
2025	J	`jnl`	Memorization and Knowledge Injection in Gated LLMs. CoRR Xu Pan, Ely Hahami, Zechen Zhang, Haim Sompolinsky
2025	J	`jnl`	Unraveling the geometry of visual relational reasoning. CoRR Jiaqi Shang, Gabriel Kreiman, Haim Sompolinsky
2025	A*	`conf`	When narrower is better: the narrow width limit of Bayesian parallel branching neural networks. ICLR Zechen Zhang, Haim Sompolinsky
2024	J	`jnl`	Coding schemes in neural networks learning classification tasks. CoRR Alexander van Meegen, Haim Sompolinsky
2024	A*	`conf`	Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers. NeurIPS Lorenzo Tiberi, Francesca Mignacco, Kazuki Irie, Haim Sompolinsky
2024	J	`jnl`	Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers. CoRR Lorenzo Tiberi, Francesca Mignacco, Kazuki Irie, Haim Sompolinsky
2024	J	`jnl`	Diverse capability and scaling of diffusion and auto-regressive models when learning abstract rules. CoRR Binxu Wang, Jiaqi Shang, Haim Sompolinsky
2024	J	`jnl`	Order parameters and phase transitions of continual learning in deep neural networks. CoRR Haozhe Shan, Qianyi Li, Haim Sompolinsky
2024	—	`conf`	Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds. CPAL Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy, Joel Dapello, James J. DiCarlo, Haim Sompolinsky, SueYeon Chung
2024	J	`jnl`	Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit. CoRR Zechen Zhang, Haim Sompolinsky
2024	J	`jnl`	Simplified derivations for high-dimensional convex learning problems. CoRR David G. Clark, Haim Sompolinsky
2023	J	`jnl`	Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime. CoRR Yehonatan Avidan, Qianyi Li, Haim Sompolinsky
2023	J	`jnl`	Optimal Quadratic Binding for Relational Reasoning in Vector Symbolic Neural Architectures. Neural Comput. Naoki Hiratani, Haim Sompolinsky
2023	J	`jnl`	Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds. CoRR Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy, Joel Dapello, James J. DiCarlo, Haim Sompolinsky, SueYeon Chung
2022	J	`jnl`	A theory of learning with constrained weight-distribution. CoRR Weishun Zhong, Ben Sorscher, Daniel D. Lee, Haim Sompolinsky
2022	A*	`conf`	A theory of weight distribution-constrained learning. NeurIPS Weishun Zhong, Ben Sorscher, Daniel Lee, Haim Sompolinsky
2022	A*	`conf`	Globally Gated Deep Linear Networks. NeurIPS Qianyi Li, Haim Sompolinsky
2022	J	`jnl`	Globally Gated Deep Linear Networks. CoRR Qianyi Li, Haim Sompolinsky
2022	J	`jnl`	Optimal quadratic binding for relational reasoning in vector symbolic neural architectures. CoRR Naoki Hiratani, Haim Sompolinsky
2022	J	`jnl`	Soft-margin classification of object manifolds. CoRR Uri Cohen, Haim Sompolinsky
2022	J	`jnl`	The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics. PLoS Comput. Biol. Yu Hu, Haim Sompolinsky
2020	J	`jnl`	A new role for circuit expansion for learning in neural networks. CoRR Julia Steinberg, Madhu Advani, Haim Sompolinsky
2020	J	`jnl`	High-dimensional dynamics of generalization error in neural networks. Neural Networks Madhu S. Advani, Andrew M. Saxe, Haim Sompolinsky
2020	J	`jnl`	Predicting the outputs of finite networks trained with noisy gradients. CoRR Gadi Naveh, Oded Ben-David, Haim Sompolinsky, Zohar Ringel
2020	J	`jnl`	Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Renormalization Group. CoRR Qianyi Li, Haim Sompolinsky
2019	J	`jnl`	Functional diversity among sensory neurons from efficient coding principles. PLoS Comput. Biol. Julijana Gjorgjieva, Markus Meister, Haim Sompolinsky
2018	J	`jnl`	Coherent chaos in a recurrent neural network with structured connectivity. PLoS Comput. Biol. Itamar Daniel Landau, Haim Sompolinsky
2018	J	`jnl`	Learning Data Manifolds with a Cutting Plane Method. Neural Comput. SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee
2017	J	`jnl`	Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity. CoRR Ran Rubin, L. F. Abbott, Haim Sompolinsky
2017	J	`jnl`	Classification and Geometry of General Perceptual Manifolds. CoRR SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
2017	J	`jnl`	Learning Data Manifolds with a Cutting Plane Method. CoRR SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee
2017	—	`conf`	Markov Transitions between Attractor States in a Recurrent Neural Network. AAAI Spring Symposia Jeremy Bernstein, Ishita Dasgupta, David Rolnick, Haim Sompolinsky
2016	—	`conf`	Optimal Architectures in a Solvable Model of Deep Networks. NIPS Jonathan Kadmon, Haim Sompolinsky
2015	J	`jnl`	Classification of Manifolds by Single-Layer Neural Networks. CoRR SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
2015	J	`jnl`	Evidence of Change of Intention in Picking Situations. J. Cogn. Neurosci. Ariel Furstenberg, Assaf Breska, Haim Sompolinsky, Leon Y. Deouell
2014	—	`ch.`	Tempotron Learning. Encyclopedia of Computational Neuroscience Robert Gütig, Haim Sompolinsky
2012	J	`jnl`	How the Brain Generates Movement. Neural Comput. Uri Rokni, Haim Sompolinsky
2011	—	`conf`	Introducing the Human Brain Project. FET Henry Markram, Karlheinz Meier, Thomas Lippert, Sten Grillner, Richard S. Frackowiak, Stanislas Dehaene, Alois C. Knoll, Haim Sompolinsky, Kris Verstreken, Javier DeFelipe, Seth Grant, Jean-Pierre Changeux, Alois Saria
2010	—	`conf`	Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics. NIPS Kanaka Rajan, L. F. Abbott, Haim Sompolinsky
2010	—	`conf`	Short-term memory in neuronal networks through dynamical compressed sensing. NIPS Surya Ganguli, Haim Sompolinsky
2009	J	`jnl`	Stimulus-Dependent Correlations in Threshold-Crossing Spiking Neurons. Neural Comput. Yoram Burak, Sam Lewallen, Haim Sompolinsky
2006	J	`jnl`	Implications of Neuronal Diversity on Population Coding. Neural Comput. Maoz Shamir, Haim Sompolinsky
2004	J	`jnl`	Nonlinear Population Codes. Neural Comput. Maoz Shamir, Haim Sompolinsky
2003	J	`jnl`	Rate Models for Conductance-Based Cortical Neuronal Networks. Neural Comput. Oren Shriki, David Hansel, Haim Sompolinsky
2001	—	`conf`	Correlation Codes in Neuronal Populations. NIPS Maoz Shamir, Haim Sompolinsky
2000	—	`conf`	An Information Maximization Approach to Overcomplete and Recurrent Representations. NIPS Oren Shriki, Haim Sompolinsky, Daniel D. Lee
1999	—	`conf`	Algorithms for Independent Components Analysis and Higher Order Statistics. NIPS Daniel D. Lee, Uri Rokni, Haim Sompolinsky
1998	J	`jnl`	Chaotic Balanced State in a Model Of Cortical Circuits. Neural Comput. Carl van Vreeswijk, Haim Sompolinsky
1998	—	`conf`	Learning a Continuous Hidden Variable Model for Binary Data. NIPS Daniel D. Lee, Haim Sompolinsky
1998	—	`conf`	The Effect of Correlations on the Fisher Information of Population Codes. NIPS Hyoungsoo Yoon, Haim Sompolinsky
1997	J	`jnl`	Traveling Waves and the Processing of Weakly Tuned Inputs in a Cortical Network Module. J. Comput. Neurosci. Rani Ben-Yishai, David Hansel, Haim Sompolinsky
1996	J	`jnl`	Chaos and synchrony in a model of a hypercolumn in visual cortex. J. Comput. Neurosci. David Hansel, Haim Sompolinsky
1996	J	`jnl`	Neural network models of perceptual learning of angle discrimination. Neural Comput. Germán Mato, Haim Sompolinsky
1994	—	`conf`	On-line Learning of Dichotomies. NIPS N. Barkai, H. Sebastian Seung, Haim Sompolinsky
1994	J	`jnl`	Segmentation by a Network of Oscillators with Stored Memories. Neural Comput. Haim Sompolinsky, Michail Tsodyks
1993	—	`conf`	Correlation Functions in a Large Stochastic Network. NIPS Iris Ginzburg, Haim Sompolinsky
1993	J	`jnl`	Stimulus-Dependent Synchronization of Neuronal Assemblies. Neural Comput. E. R. Grannan, D. Kleinfeld, Haim Sompolinsky
1992	J	`jnl`	Processing of Sensory Information by a Network of Oscillators with Memory. Int. J. Neural Syst. Haim Sompolinsky, Michail Tsodyks
1992	A*	`conf`	Query by Committee. COLT H. Sebastian Seung, Manfred Opper, Haim Sompolinsky
1991	A*	`conf`	Learning Curves in Large Neural Networks. COLT H. Sebastian Seung, Haim Sompolinsky, Naftali Tishby