Short-term memory in neuronal networks through dynamical compressed sensing. - Surya Ganguli; Haim Sompolinsky (NIPS, 2010)

Learning hierarchical categories in deep neural networks. - Andrew M. Saxe; James L. McClelland; Surya Ganguli (CogSci, 2013)

Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness. - Jonathan C. Kao; Paul Nuyujukian; Sergey D. Stavisky; Stephen I. Ryu; Surya Ganguli; Krishna V. Shenoy (EMBC, 2013)

A memory frontier for complex synapses. - Subhaneil Lahiri; Surya Ganguli (NIPS, 2013)

An adaptive low dimensional quasi-Newton sum of functions optimizer. - Jascha Sohl-Dickstein; Ben Poole; Surya Ganguli (CoRR, 2013)

Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. - Jascha Sohl-Dickstein; Ben Poole; Surya Ganguli (ICML, 2014)

Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. - Yann N. Dauphin; Razvan Pascanu; Çaglar Gülçehre; KyungHyun Cho; Surya Ganguli; Yoshua Bengio (NIPS, 2014)

On the saddle point problem for non-convex optimization. - Razvan Pascanu; Yann N. Dauphin; Surya Ganguli; Yoshua Bengio (CoRR, 2014)

Analyzing noise in autoencoders and deep networks. - Ben Poole; Jascha Sohl-Dickstein; Surya Ganguli (CoRR, 2014)

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. - Andrew M. Saxe; James L. McClelland; Surya Ganguli (ICLR, 2014)

Deep Unsupervised Learning using Nonequilibrium Thermodynamics. - Jascha Sohl-Dickstein; Eric A. Weiss; Niru Maheswaranathan; Surya Ganguli (ICML, 2015)

Deep Knowledge Tracing. - Chris Piech; Jonathan Bassen; Jonathan Huang; Surya Ganguli; Mehran Sahami; Leonidas J. Guibas; Jascha Sohl-Dickstein (NIPS, 2015)

Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences. - Kristofer E. Bouchard; Surya Ganguli; Michael S. Brainard (Frontiers Comput. Neurosci., 2015)

An equivalence between high dimensional Bayes optimal inference and M-estimation. - Madhu Advani; Surya Ganguli (NIPS, 2016)

Deep Learning Models of the Retinal Response to Natural Scenes. - Lane McIntosh; Niru Maheswaranathan; Aran Nayebi; Surya Ganguli; Stephen Baccus (NIPS, 2016)

Exponential expressivity in deep neural networks through transient chaos. - Ben Poole; Subhaneil Lahiri; Maithra Raghu; Jascha Sohl-Dickstein; Surya Ganguli (NIPS, 2016)

Random projections of random manifolds. - Subhaneil Lahiri; Peiran Gao; Surya Ganguli (CoRR, 2016)

A universal tradeoff between power, precision and speed in physical communication. - Subhaneil Lahiri; Jascha Sohl-Dickstein; Surya Ganguli (CoRR, 2016)

Survey of Expressivity in Deep Neural Networks. - Maithra Raghu; Ben Poole; Jon M. Kleinberg; Surya Ganguli; Jascha Sohl-Dickstein (CoRR, 2016)

Intelligent synapses for multi-task and transfer learning. - Ben Poole; Friedemann Zenke; Surya Ganguli (ICLR (Workshop), 2017)

Deep Information Propagation. - Samuel S. Schoenholz; Justin Gilmer; Surya Ganguli; Jascha Sohl-Dickstein (ICLR (Poster), 2017)

On the Expressive Power of Deep Neural Networks. - Maithra Raghu; Ben Poole; Jon M. Kleinberg; Surya Ganguli; Jascha Sohl-Dickstein (ICML, 2017)

Continual Learning Through Synaptic Intelligence. - Friedemann Zenke; Ben Poole; Surya Ganguli (ICML, 2017)

Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. - Anirudh Goyal; Nan Rosemary Ke; Surya Ganguli; Yoshua Bengio (NIPS, 2017)

Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. - Jeffrey Pennington; Samuel S. Schoenholz; Surya Ganguli (NIPS, 2017)

Biologically inspired protection of deep networks from adversarial attacks. - Aran Nayebi; Surya Ganguli (CoRR, 2017)

SuperSpike: Supervised learning in multi-layer spiking neural networks. - Friedemann Zenke; Surya Ganguli (CoRR, 2017)

Improved multitask learning through synaptic intelligence. - Friedemann Zenke; Ben Poole; Surya Ganguli (CoRR, 2017)

Pyret: A Python package for analysis of neurophysiology data. - Benjamin Naecker; Niru Maheswaranathan; Surya Ganguli; Stephen Baccus (J. Open Source Softw., 2017)

The emergence of spectral universality in deep networks. - Jeffrey Pennington; Samuel S. Schoenholz; Surya Ganguli (AISTATS, 2018)

Statistical mechanics of low-rank tensor decomposition. - Jonathan Kadmon; Surya Ganguli (NeurIPS, 2018)

Task-Driven Convolutional Recurrent Models of the Visual System. - Aran Nayebi; Daniel Bear; Jonas Kubilius; Kohitij Kar; Surya Ganguli; David Sussillo; James J. DiCarlo; Daniel L. K. Yamins (NeurIPS, 2018)

The emergence of multiple retinal cell types through efficient coding of natural movies. - Samuel A. Ocko; Jack Lindsey; Surya Ganguli; Stéphane Deny (NeurIPS, 2018)

A mathematical theory of semantic development in deep neural networks. - Andrew M. Saxe; James L. McClelland; Surya Ganguli (CoRR, 2018)

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks. - Friedemann Zenke; Surya Ganguli (Neural Comput., 2018)

Inferring hidden structure in multilayered neural circuits. - Niru Maheswaranathan; David B. Kastner; Stephen A. Baccus; Surya Ganguli (PLoS Comput. Biol., 2018)

An analytic theory of generalization dynamics and transfer learning in deep linear networks. - Andrew K. Lampinen; Surya Ganguli (ICLR (Poster), 2019)

A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. - Jack Lindsey; Samuel A. Ocko; Surya Ganguli; Stéphane Deny (ICLR, 2019)

Universality and individuality in neural dynamics across large populations of recurrent networks. - Niru Maheswaranathan; Alex H. Williams; Matthew D. Golub; Surya Ganguli; David Sussillo (NeurIPS, 2019)

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. - Niru Maheswaranathan; Alex H. Williams; Matthew D. Golub; Surya Ganguli; David Sussillo (NeurIPS, 2019)

A unified theory for the origin of grid cells through the lens of pattern formation. - Ben Sorscher; Gabriel Mel; Surya Ganguli; Samuel A. Ocko (NeurIPS, 2019)

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. - Hidenori Tanaka; Aran Nayebi; Niru Maheswaranathan; Lane McIntosh; Stephen Baccus; Surya Ganguli (NeurIPS, 2019)

Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates. - Anthony Degleris; Ben Antin; Surya Ganguli; Alex H. Williams (CoRR, 2019)

Emergent properties of the local geometry of neural loss landscapes. - Stanislav Fort; Surya Ganguli (CoRR, 2019)

RNNs can generate bounded hierarchical languages with optimal memory. - John Hewitt; Michael Hahn 001; Surya Ganguli; Percy Liang; Christopher D. Manning (EMNLP (1), 2020)

Two Routes to Scalable Credit Assignment without Weight Symmetry. - Daniel Kunin; Aran Nayebi; Javier Sagastuy-Breña; Surya Ganguli; Jonathan M. Bloom; Daniel Yamins (ICML, 2020)

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. - Stanislav Fort; Gintare Karolina Dziugaite; Mansheej Paul; Sepideh Kharaghani; Daniel M. Roy 001; Surya Ganguli (NeurIPS, 2020)

Predictive coding in balanced neural networks with noise, chaos and delays. - Jonathan Kadmon; Jonathan Timcheck; Surya Ganguli (NeurIPS, 2020)

Identifying Learning Rules From Neural Network Observables. - Aran Nayebi; Sanjana Srivastava; Surya Ganguli; Daniel L. K. Yamins (NeurIPS, 2020)

Pruning neural networks without any data by iteratively conserving synaptic flow. - Hidenori Tanaka; Daniel Kunin; Daniel L. K. Yamins; Surya Ganguli (NeurIPS, 2020)

Understanding Self-supervised Learning with Dual Deep Networks. - Yuandong Tian; Lantao Yu; Xinlei Chen; Surya Ganguli (CoRR, 2020)

Fundamental bounds on the fidelity of sensory cortical coding. - Oleg I. Rumyantsev; Jérôme A. Lecoq; Oscar Hernandez; Yanping Zhang; Joan Savall; Radoslaw Chrapkiewicz; Jane Li; Hongkui Zeng; Surya Ganguli; Mark J. Schnitzer (Nat., 2020)

Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. - Daniel Kunin; Javier Sagastuy-Breña; Surya Ganguli; Daniel L. K. Yamins; Hidenori Tanaka (ICLR, 2021)

A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions. - Gabriel Mel; Surya Ganguli (ICML, 2021)

Understanding self-supervised learning dynamics without contrastive pairs. - Yuandong Tian; Xinlei Chen; Surya Ganguli (ICML, 2021)

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks. - Aran Nayebi; Alexander Attinger; Malcolm Campbell; Kiah Hardcastle; Isabel Low; Caitlin S. Mallory; Gabriel Mel; Ben Sorscher; Alex H. Williams; Surya Ganguli; Lisa M. Giocomo; Daniel L. K. Yamins (NeurIPS, 2021)

Deep Learning on a Data Diet: Finding Important Examples Early in Training. - Mansheej Paul; Surya Ganguli; Gintare Karolina Dziugaite (NeurIPS, 2021)

Embodied Intelligence via Learning and Evolution. - Agrim Gupta; Silvio Savarese; Surya Ganguli; Li Fei-Fei 001 (CoRR, 2021)

Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion. - Daniel Kunin; Javier Sagastuy-Breña; Lauren E. Gillespie; Eshed Margalit; Hidenori Tanaka; Surya Ganguli; Daniel L. K. Yamins (CoRR, 2021)

MetaMorph: Learning Universal Controllers with Transformers. - Agrim Gupta; Linxi Fan; Surya Ganguli; Li Fei-Fei 001 (ICLR, 2022)

How many degrees of freedom do we need to train deep networks: a loss landscape perspective. - Brett W. Larsen; Stanislav Fort; Nic Becker; Surya Ganguli (ICLR, 2022)

Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. - Mansheej Paul; Brett W. Larsen; Surya Ganguli; Jonathan Frankle; Gintare Karolina Dziugaite (NeurIPS, 2022)

Beyond neural scaling laws: beating power law scaling via data pruning. - Ben Sorscher; Robert Geirhos; Shashank Shekhar; Surya Ganguli; Ari Morcos (NeurIPS, 2022)

Disentangling with Biological Constraints: A Theory of Functional Cell Types. - James C. R. Whittington; Will Dorrell; Surya Ganguli; Timothy Edward John Behrens (CoRR, 2022)

What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries. - Stanislav Fort; Ekin Dogus Cubuk; Surya Ganguli; Samuel S. Schoenholz (CoRR, 2022)

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. - Anthony Zador; Blake A. Richards; Bence Ölveczky; Sean Escola; Yoshua Bengio; Kwabena Boahen 001; Matthew M. Botvinick; Dmitri B. Chklovskii; Anne Churchland; Claudia Clopath; James DiCarlo; Surya Ganguli; et al. (CoRR, 2022)

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition. - Aran Nayebi; Javier Sagastuy-Breña; Daniel M. Bear; Kohitij Kar; Jonas Kubilius; Surya Ganguli; David Sussillo; James J. DiCarlo; Daniel L. K. Yamins (Neural Comput., 2022)

Synaptic balancing: A biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance. - Christopher H. Stock; Sarah E. Harvey; Samuel A. Ocko; Surya Ganguli (PLoS Comput. Biol., 2022)

Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays. - Jonathan Timcheck; Jonathan Kadmon; Kwabena Boahen 001; Surya Ganguli (PLoS Comput. Biol., 2022)

The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. - Daniel Kunin; Atsushi Yamamura; Chao Ma; Surya Ganguli (ICLR, 2023)

Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? - Mansheej Paul; Feng Chen 046; Brett W. Larsen; Jonathan Frankle; Surya Ganguli; Gintare Karolina Dziugaite (ICLR, 2023)

Disentanglement with Biological Constraints: A Theory of Functional Cell Types. - James C. R. Whittington; Will Dorrell; Surya Ganguli; Timothy Behrens (ICLR, 2023)

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks. - Feng Chen 046; Daniel Kunin; Atsushi Yamamura; Surya Ganguli (NeurIPS, 2023)

Information Geometry of the Retinal Representation Manifold. - Xuehao Ding; Dongsoo Lee; Joshua Melander; George Sivulka; Surya Ganguli; Stephen Baccus (NeurIPS, 2023)

Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression. - Allan Raventós; Mansheej Paul; Feng Chen 046; Surya Ganguli (NeurIPS, 2023)

SemDeDup: Data-efficient learning at web-scale through semantic deduplication. - Amro Abbas; Kushal Tirumala; Daniel Simig; Surya Ganguli; Ari S. Morcos (CoRR, 2023)

Holistic Evaluation of Language Models. - Percy Liang; Rishi Bommasani; Tony Lee; Dimitris Tsipras; Dilara Soylu; Michihiro Yasunaga; Yian Zhang; Deepak Narayanan; Yuhuai Wu; Ananya Kumar; Benjamin Newman; Binhang Yuan; et al. (Trans. Mach. Learn. Res., 2023)

Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning. - Daniel Kunin; Allan Raventós; Clémentine C. J. Dominé; Feng Chen 046; David A. Klindt; Andrew M. Saxe; Surya Ganguli (NeurIPS, 2024)

Geometric Dynamics of Signal Propagation Predict Trainability of Transformers. - Aditya Cowsik; Tamra Nebabu; Xiao-Liang Qi; Surya Ganguli (CoRR, 2024)

Features are fate: a theory of transfer learning in high-dimensional regression. - Javan Tahir; Surya Ganguli; Grant M. Rotskoff (CoRR, 2024)

Fooling LLM graders into giving better grades through neural activity guided adversarial prompting. - Atsushi Yamamura; Surya Ganguli (CoRR, 2024)

An analytic theory of creativity in convolutional diffusion models. - Mason Kamb; Surya Ganguli (CoRR, 2024)

The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion. - Daniel Kunin; Javier Sagastuy-Breña; Lauren E. Gillespie; Eshed Margalit; Hidenori Tanaka; Surya Ganguli; Daniel L. K. Yamins (Neural Comput., 2024)

Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning. - Feng Chen 046; Allan Raventós; Nan Cheng; Surya Ganguli; Shaul Druckmann (CoRR, 2025)

Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks. - Daniel Kunin; Giovanni Luca Marchetti; Feng Chen; Dhruva Karkada; James B. Simon; Michael Robert DeWeese; Surya Ganguli; Nina Miolane (CoRR, 2025)

CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers. - Haining Pan; James V. Roggeveen; Erez Berg; Juan Carrasquilla; Debanjan Chowdhury; Surya Ganguli; Federico Ghimenti; Juraj Hasik; Henry Hunt; HongJiang Zhang; Mason Kamb; Ying-Jer Kao; et al. (CoRR, 2025)