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2020 – today
- 2024
[j10]Daniel Kunin, Javier Sagastuy-Breña, Lauren Gillespie
, Eshed Margalit, Hidenori Tanaka, Surya Ganguli, Daniel L. K. Yamins:
The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion. Neural Comput. 36(1): 151-174 (2024)- [i54]Aditya Cowsik, Tamra Nebabu, Xiao-Liang Qi, Surya Ganguli:
Geometric Dynamics of Signal Propagation Predict Trainability of Transformers. CoRR abs/2403.02579 (2024) - [i53]Daniel Kunin, Allan Raventós, Clémentine Dominé, Feng Chen, David Klindt, Andrew M. Saxe, Surya Ganguli:
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning. CoRR abs/2406.06158 (2024) - 2023
- [j9]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 2023 (2023) - [c49]Daniel Kunin, Atsushi Yamamura, Chao Ma, Surya Ganguli:
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. ICLR 2023 - [c48]Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite:
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? ICLR 2023 - [c47]James C. R. Whittington, Will Dorrell, Surya Ganguli, Timothy Behrens:
Disentanglement with Biological Constraints: A Theory of Functional Cell Types. ICLR 2023 - [c46]Feng Chen, Daniel Kunin, Atsushi Yamamura, Surya Ganguli:
Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks. NeurIPS 2023 - [c45]Xuehao Ding, Dongsoo Lee, Joshua Melander, George Sivulka, Surya Ganguli, Stephen Baccus:
Information Geometry of the Retinal Representation Manifold. NeurIPS 2023 - [c44]Allan Raventós, Mansheej Paul, Feng Chen, Surya Ganguli:
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression. NeurIPS 2023 - [i52]Amro Abbas, Kushal Tirumala, Daniel Simig, Surya Ganguli, Ari S. Morcos:
SemDeDup: Data-efficient learning at web-scale through semantic deduplication. CoRR abs/2303.09540 (2023) - [i51]Feng Chen, Daniel Kunin, Atsushi Yamamura, Surya Ganguli:
Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks. CoRR abs/2306.04251 (2023) - [i50]Allan Raventós, Mansheej Paul, Feng Chen, Surya Ganguli:
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression. CoRR abs/2306.15063 (2023) - 2022
- [j8]Aran Nayebi, Javier Sagastuy-Breña, Daniel M. Bear, Kohitij Kar, Jonas Kubilius, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition. Neural Comput. 34(8): 1652-1675 (2022) - [j7]Christopher H. Stock, Sarah E. Harvey, Samuel A. Ocko, Surya Ganguli:
Synaptic balancing: A biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance. PLoS Comput. Biol. 18(9): 1010418 (2022) - [j6]Jonathan Timcheck, Jonathan Kadmon, Kwabena Boahen, Surya Ganguli:
Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays. PLoS Comput. Biol. 18(10): 1010593 (2022) - [c43]Agrim Gupta, Linxi Fan, Surya Ganguli, Li Fei-Fei:
MetaMorph: Learning Universal Controllers with Transformers. ICLR 2022 - [c42]Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli:
How many degrees of freedom do we need to train deep networks: a loss landscape perspective. ICLR 2022 - [c41]Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite:
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. NeurIPS 2022 - [c40]Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari Morcos:
Beyond neural scaling laws: beating power law scaling via data pruning. NeurIPS 2022 - [i49]Agrim Gupta, Linxi Fan, Surya Ganguli, Li Fei-Fei:
MetaMorph: Learning Universal Controllers with Transformers. CoRR abs/2203.11931 (2022) - [i48]Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite:
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. CoRR abs/2206.01278 (2022) - [i47]Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari S. Morcos:
Beyond neural scaling laws: beating power law scaling via data pruning. CoRR abs/2206.14486 (2022) - [i46]James C. R. Whittington, Will Dorrell, Surya Ganguli, Timothy Edward John Behrens:
Disentangling with Biological Constraints: A Theory of Functional Cell Types. CoRR abs/2210.01768 (2022) - [i45]Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite:
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? CoRR abs/2210.03044 (2022) - [i44]Daniel Kunin, Atsushi Yamamura, Chao Ma, Surya Ganguli:
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. CoRR abs/2210.03820 (2022) - [i43]Stanislav Fort, Ekin Dogus Cubuk, Surya Ganguli, Samuel S. Schoenholz:
What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries. CoRR abs/2210.05546 (2022) - [i42]Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy P. Lillicrap, Adam H. Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Tsao:
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR abs/2210.08340 (2022) - [i41]Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda:
Holistic Evaluation of Language Models. CoRR abs/2211.09110 (2022) - 2021
- [c39]Daniel Kunin, Javier Sagastuy-Breña, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka:
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. ICLR 2021 - [c38]Gabriel Mel, Surya Ganguli:
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. ICML 2021: 7578-7587 - [c37]Yuandong Tian, Xinlei Chen, Surya Ganguli:
Understanding self-supervised learning dynamics without contrastive pairs. ICML 2021: 10268-10278 - [c36]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:
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks. NeurIPS 2021: 12167-12179 - [c35]Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite:
Deep Learning on a Data Diet: Finding Important Examples Early in Training. NeurIPS 2021: 20596-20607 - [i40]Agrim Gupta, Silvio Savarese, Surya Ganguli, Li Fei-Fei:
Embodied Intelligence via Learning and Evolution. CoRR abs/2102.02202 (2021) - [i39]Yuandong Tian, Xinlei Chen, Surya Ganguli:
Understanding self-supervised Learning Dynamics without Contrastive Pairs. CoRR abs/2102.06810 (2021) - [i38]Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli:
How many degrees of freedom do we need to train deep networks: a loss landscape perspective. CoRR abs/2107.05802 (2021) - [i37]Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite:
Deep Learning on a Data Diet: Finding Important Examples Early in Training. CoRR abs/2107.07075 (2021) - [i36]Daniel Kunin, Javier Sagastuy-Breña, Lauren Gillespie, Eshed Margalit, Hidenori Tanaka, Surya Ganguli, Daniel L. K. Yamins:
Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion. CoRR abs/2107.09133 (2021) - 2020
- [j5]Oleg I. Rumyantsev, Jérôme A. Lecoq, Oscar Hernandez, Yanping Zhang, Joan Savall, Radoslaw Chrapkiewicz, Jane Li, Hongkui Zeng, Surya Ganguli, Mark J. Schnitzer:
Fundamental bounds on the fidelity of sensory cortical coding. Nat. 580(7801): 100-105 (2020) - [c34]John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang, Christopher D. Manning:
RNNs can generate bounded hierarchical languages with optimal memory. EMNLP (1) 2020: 1978-2010 - [c33]Daniel Kunin, Aran Nayebi, Javier Sagastuy-Breña, Surya Ganguli, Jonathan M. Bloom, Daniel Yamins:
Two Routes to Scalable Credit Assignment without Weight Symmetry. ICML 2020: 5511-5521 - [c32]Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. NeurIPS 2020 - [c31]Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli:
Predictive coding in balanced neural networks with noise, chaos and delays. NeurIPS 2020 - [c30]Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins:
Identifying Learning Rules From Neural Network Observables. NeurIPS 2020 - [c29]Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. NeurIPS 2020 - [i35]Daniel Kunin, Aran Nayebi, Javier Sagastuy-Breña, Surya Ganguli, Jonathan M. Bloom, Daniel L. K. Yamins:
Two Routes to Scalable Credit Assignment without Weight Symmetry. CoRR abs/2003.01513 (2020) - [i34]Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. CoRR abs/2006.05467 (2020) - [i33]Yuandong Tian, Lantao Yu, Xinlei Chen, Surya Ganguli:
Understanding Self-supervised Learning with Dual Deep Networks. CoRR abs/2010.00578 (2020) - [i32]John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang, Christopher D. Manning:
RNNs can generate bounded hierarchical languages with optimal memory. CoRR abs/2010.07515 (2020) - [i31]Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins:
Identifying Learning Rules From Neural Network Observables. CoRR abs/2010.11765 (2020) - [i30]Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. CoRR abs/2010.15110 (2020) - [i29]Daniel Kunin, Javier Sagastuy-Breña, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka:
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. CoRR abs/2012.04728 (2020)
2010 – 2019
- 2019
- [c28]Andrew K. Lampinen, Surya Ganguli:
An analytic theory of generalization dynamics and transfer learning in deep linear networks. ICLR (Poster) 2019 - [c27]Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stéphane Deny:
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. ICLR 2019 - [c26]Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli:
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. NeurIPS 2019: 8535-8545 - [c25]Ben Sorscher, Gabriel Mel, Surya Ganguli, Samuel A. Ocko:
A unified theory for the origin of grid cells through the lens of pattern formation. NeurIPS 2019: 10003-10013 - [c24]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Universality and individuality in neural dynamics across large populations of recurrent networks. NeurIPS 2019: 15603-15615 - [c23]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. NeurIPS 2019: 15670-15679 - [i28]Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stéphane Deny:
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. CoRR abs/1901.00945 (2019) - [i27]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. CoRR abs/1906.10720 (2019) - [i26]Anthony Degleris, Ben Antin, Surya Ganguli, Alex H. Williams:
Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates. CoRR abs/1907.00139 (2019) - [i25]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Universality and individuality in neural dynamics across large populations of recurrent networks. CoRR abs/1907.08549 (2019) - [i24]Stanislav Fort, Surya Ganguli:
Emergent properties of the local geometry of neural loss landscapes. CoRR abs/1910.05929 (2019) - [i23]Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen A. Baccus, Surya Ganguli:
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. CoRR abs/1912.06207 (2019) - 2018
- [j4]Friedemann Zenke, Surya Ganguli:
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks. Neural Comput. 30(6) (2018) - [j3]Niru Maheswaranathan, David B. Kastner, Stephen A. Baccus, Surya Ganguli:
Inferring hidden structure in multilayered neural circuits. PLoS Comput. Biol. 14(8) (2018) - [c22]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
The emergence of spectral universality in deep networks. AISTATS 2018: 1924-1932 - [c21]Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Task-Driven Convolutional Recurrent Models of the Visual System. NeurIPS 2018: 5295-5306 - [c20]Jonathan Kadmon, Surya Ganguli:
Statistical mechanics of low-rank tensor decomposition. NeurIPS 2018: 8212-8222 - [c19]Samuel A. Ocko, Jack Lindsey, Surya Ganguli, Stéphane Deny:
The emergence of multiple retinal cell types through efficient coding of natural movies. NeurIPS 2018: 9411-9422 - [i22]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
The Emergence of Spectral Universality in Deep Networks. CoRR abs/1802.09979 (2018) - [i21]Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Task-Driven Convolutional Recurrent Models of the Visual System. CoRR abs/1807.00053 (2018) - [i20]Andrew K. Lampinen, Surya Ganguli:
An analytic theory of generalization dynamics and transfer learning in deep linear networks. CoRR abs/1809.10374 (2018) - [i19]Jonathan Kadmon, Surya Ganguli:
Statistical mechanics of low-rank tensor decomposition. CoRR abs/1810.10065 (2018) - [i18]Andrew M. Saxe, James L. McClelland, Surya Ganguli:
A mathematical theory of semantic development in deep neural networks. CoRR abs/1810.10531 (2018) - 2017
- [j2]Benjamin Naecker, Niru Maheswaranathan, Surya Ganguli, Stephen Baccus:
Pyret: A Python package for analysis of neurophysiology data. J. Open Source Softw. 2(9): 137 (2017) - [c18]Ben Poole, Friedemann Zenke, Surya Ganguli:
Intelligent synapses for multi-task and transfer learning. ICLR (Workshop) 2017 - [c17]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. ICLR (Poster) 2017 - [c16]Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein:
On the Expressive Power of Deep Neural Networks. ICML 2017: 2847-2854 - [c15]Friedemann Zenke, Ben Poole, Surya Ganguli:
Continual Learning Through Synaptic Intelligence. ICML 2017: 3987-3995 - [c14]Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio:
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. NIPS 2017: 4392-4402 - [c13]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. NIPS 2017: 4785-4795 - [i17]Friedemann Zenke, Ben Poole, Surya Ganguli:
Improved multitask learning through synaptic intelligence. CoRR abs/1703.04200 (2017) - [i16]Aran Nayebi, Surya Ganguli:
Biologically inspired protection of deep networks from adversarial attacks. CoRR abs/1703.09202 (2017) - [i15]Friedemann Zenke, Surya Ganguli:
SuperSpike: Supervised learning in multi-layer spiking neural networks. CoRR abs/1705.11146 (2017) - [i14]Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio:
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. CoRR abs/1711.02282 (2017) - [i13]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. CoRR abs/1711.04735 (2017) - 2016
- [c12]Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus:
Deep Learning Models of the Retinal Response to Natural Scenes. NIPS 2016: 1361-1369 - [c11]Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli:
Exponential expressivity in deep neural networks through transient chaos. NIPS 2016: 3360-3368 - [c10]Madhu Advani, Surya Ganguli:
An equivalence between high dimensional Bayes optimal inference and M-estimation. NIPS 2016: 3378-3386 - [i12]Subhaneil Lahiri, Jascha Sohl-Dickstein, Surya Ganguli:
A universal tradeoff between power, precision and speed in physical communication. CoRR abs/1603.07758 (2016) - [i11]Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein:
On the expressive power of deep neural networks. CoRR abs/1606.05336 (2016) - [i10]Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli:
Exponential expressivity in deep neural networks through transient chaos. CoRR abs/1606.05340 (2016) - [i9]Subhaneil Lahiri, Peiran Gao, Surya Ganguli:
Random projections of random manifolds. CoRR abs/1607.04331 (2016) - [i8]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. CoRR abs/1611.01232 (2016) - [i7]Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein:
Survey of Expressivity in Deep Neural Networks. CoRR abs/1611.08083 (2016) - 2015
- [j1]Kristofer E. Bouchard, Surya Ganguli, Michael S. Brainard:
Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences. Frontiers Comput. Neurosci. 9: 92 (2015) - [c9]Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli:
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. ICML 2015: 2256-2265 - [c8]Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein:
Deep Knowledge Tracing. NIPS 2015: 505-513 - [i6]Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli:
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. CoRR abs/1503.03585 (2015) - [i5]Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein:
Deep Knowledge Tracing. CoRR abs/1506.05908 (2015) - 2014
- [c7]Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli:
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. ICML 2014: 604-612 - [c6]Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, KyungHyun Cho, Surya Ganguli, Yoshua Bengio:
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. NIPS 2014: 2933-2941 - [c5]Andrew M. Saxe, James L. McClelland, Surya Ganguli:
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. ICLR 2014 - [i4]Razvan Pascanu, Yann N. Dauphin, Surya Ganguli, Yoshua Bengio:
On the saddle point problem for non-convex optimization. CoRR abs/1405.4604 (2014) - [i3]Ben Poole, Jascha Sohl-Dickstein, Surya Ganguli:
Analyzing noise in autoencoders and deep networks. CoRR abs/1406.1831 (2014) - [i2]Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio:
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. CoRR abs/1406.2572 (2014) - 2013
- [c4]Andrew M. Saxe, James L. McClelland, Surya Ganguli:
Learning hierarchical categories in deep neural networks. CogSci 2013 - [c3]Jonathan C. Kao, Paul Nuyujukian, Sergey D. Stavisky, Stephen I. Ryu, Surya Ganguli, Krishna V. Shenoy:
Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness. EMBC 2013: 293-298 - [c2]Subhaneil Lahiri, Surya Ganguli:
A memory frontier for complex synapses. NIPS 2013: 1034-1042 - [i1]Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli:
An adaptive low dimensional quasi-Newton sum of functions optimizer. CoRR abs/1311.2115 (2013) - 2010
- [c1]Surya Ganguli, Haim Sompolinsky:
Short-term memory in neuronal networks through dynamical compressed sensing. NIPS 2010: 667-675
Coauthor Index
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