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Ganguli, Surya

  1. An analytic theory of creativity in convolutional diffusion models
    2024/12/28 by Mason Kamb, Kamb, Mason, Surya Ganguli +1 · 16 voices · 26 citations
    Psychology · #Creativity in Education and Neuroscience #cond-mat.dis-nn #cs.AI #cs.LG #q-bio.NC #stat.ML
  2. Deep Unsupervised Learning using Nonequilibrium Thermodynamics
    2015/03/12 by Jascha Sohl‐Dickstein, Eric A. Weiss, Sohl-Dickstein, Jascha +5 · 796 citations
    Computer Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neurons and Cognition (q-bio.NC)
  3. Exponential expressivity in deep neural networks through transient chaos
    2016/06/16 by Ben Poole, Subhaneil Lahiri, Poole, Ben +8 · 2 voices · 37 citations
    Computer Science · Mathematics · Neuroscience · Physics and Astronomy · #Model Reduction and Neural Networks #Neural Networks and Applications #Neural dynamics and brain function #cond-mat.dis-nn #cs.LG #stat.ML
  4. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
    2013/12/20 by Andrew Saxe, Andrew M. Saxe, Saxe, Andrew M. +4 · 1 voice · 146 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Gaussian Processes and Bayesian Inference #Model Reduction and Neural Networks #Neural Networks and Applications #cond-mat.dis-nn #cs.CV #cs.LG #cs.NE #q-bio.NC #stat.ML
  5. Deep Knowledge Tracing
    2015/06/19 by Chris Piech, Piech, Chris, Jonathan Huang +10 · 55 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #K.3.1 #Machine Learning (cs.LG) #Online Learning and Analytics #Topic Modeling
  6. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
    2014/06/10 by Dauphin, Yann, Pascanu, Razvan, Gulcehre, Caglar +3 · 33 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  7. Beyond neural scaling laws: beating power law scaling via data pruning
    2022/06/29 by Ben Sorscher, Robert Geirhos, Sorscher, Ben +7 · 54 citations
    Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  8. On the Expressive Power of Deep Neural Networks
    2016/06/16 by Maithra Raghu, Raghu, Maithra, Ben Poole +7 · 39 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Neural Networks and Applications #Machine Learning and Algorithms
  9. SemDeDup: Data-efficient learning at web-scale through semantic deduplication
    2023/03/16 by Amro Abbas, Abbas, Amro, Kushal Tirumala +7 · 34 citations
    Decision Sciences · Computer Science · #Data Quality and Management #Topic Modeling #Privacy-Preserving Technologies in Data
  10. Deep Information Propagation
    2016/11/04 by Samuel S. Schoenholz, Schoenholz, Samuel S., Justin Gilmer +5 · 19 citations
    Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Statistical Mechanics and Entropy
  11. Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
    2017/11/13 by Pennington, Jeffrey, Schoenholz, Samuel S., Ganguli, Surya · 14 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  12. Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
    2023/06/26 by Allan Raventós, Raventós, Allan, Mansheej Paul +5 · 23 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Distributed Sensor Networks and Detection Algorithms #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG)
  13. Universality and individuality in neural dynamics across large populations of recurrent networks
    2019/07/19 by Niru Maheswaranathan, Maheswaranathan, Niru, Alex H. Williams +7 · 13 citations
    Neuroscience · Computer Science · #Functional Brain Connectivity Studies #Neural dynamics and brain function #Neural Networks and Applications
  14. Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
    2022/10/15 by Anthony M. Zador, G. Sean Escola, Zador, Anthony +51 · 15 citations
    Computer Science · Biochemistry, Genetics and Molecular Biology · #Reinforcement Learning in Robotics #Computability, Logic, AI Algorithms #Cell Image Analysis Techniques
  15. On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience
    2015/03/30 by Gao, Peiran, Ganguli, Surya · 8 citations
    #FOS: Biological sciences #Neurons and Cognition (q-bio.NC)
  16. Understanding self-supervised Learning Dynamics without Contrastive Pairs
    2021/02/12 by Yuandong Tian, Tian, Yuandong, Xinlei Chen +3 · 14 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Advanced Neural Network Applications #Multimodal Machine Learning Applications
  17. MetaMorph: Learning Universal Controllers with Transformers
    2022/03/22 by Agrim Gupta, Gupta, Agrim, Linxi Fan +5 · 12 citations
    Engineering · #Modular Robots and Swarm Intelligence #Robot Manipulation and Learning
  18. An analytic theory of generalization dynamics and transfer learning in deep linear networks
    2018/09/27 by Andrew K. Lampinen, Lampinen, Andrew K., Surya Ganguli +1 · 11 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Stochastic Gradient Optimization Techniques #Neural Networks and Applications
  19. The Emergence of Spectral Universality in Deep Networks
    2018/02/27 by Jeffrey Pennington, Pennington, Jeffrey, Samuel S. Schoenholz +3 · 12 citations
    Computer Science · Engineering · #Blind Source Separation Techniques #Image and Signal Denoising Methods #Sparse and Compressive Sensing Techniques
  20. Disentanglement with Biological Constraints: A Theory of Functional Cell Types
    2022/09/30 by James C. R. Whittington, Will Dorrell, Whittington, James C. R. +7 · 1 voice · 9 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Cell Image Analysis Techniques #Neural Networks and Applications #Neural dynamics and brain function #cs.LG #cs.NE #q-bio.NC
  21. On the saddle point problem for non-convex optimization
    2014/05/19 by Razvan Pascanu, Yann Dauphin, Pascanu, Razvan +5 · 9 citations
    Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference
  22. Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
    2024/06/10 by Daniel Kunin, Kunin, Daniel, Allan Raventós +11 · 4 voices · 6 citations
    Computer Science · #Machine Learning and Data Classification
  23. Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning\n Dynamics
    2020/12/08 by Daniel Kunin, Kunin, Daniel, Javier Sagastuy-Breña +7 · 10 citations
    Computer Science · #Stochastic Gradient Optimization Techniques #Adversarial Robustness in Machine Learning #Generative Adversarial Networks and Image Synthesis
  24. Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
    2020/10/28 by Stanislav Fort, Fort, Stanislav, Gintare Karolina Dziugaite +9 · 8 citations
    Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Stochastic Gradient Optimization Techniques
  25. RNNs can generate bounded hierarchical languages with optimal memory
    2020/10/15 by Hewitt, John, Hahn, Michael, Ganguli, Surya +2 · 6 citations
    #Computation and Language (cs.CL) #FOS: Computer and information sciences
  26. Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods
    2013/11/08 by Jascha Sohl-Dickstein, Jascha Sohl‐Dickstein, Sohl-Dickstein, Jascha +4 · 1 voice · 3 citations
    Computer Science · Engineering · #90C26 #Advanced Image Processing Techniques #FOS: Computer and information sciences #G.1.6 #Machine Learning (cs.LG) #Neural Networks and Applications #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #cs.LG
  27. Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
    2023/06/07 by Feng Chen, Daniel Kunin, Chen, Feng +5 · 7 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Stochastic Gradient Optimization Techniques
  28. Emergent properties of the local geometry of neural loss landscapes
    2019/10/14 by Fort, Stanislav, Ganguli, Surya · 4 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
  29. Two Routes to Scalable Credit Assignment without Weight Symmetry
    2020/02/28 by Daniel Kunin, Kunin, Daniel, Aran Nayebi +9 · 3 citations
    Computer Science · Engineering · #Advanced Memory and Neural Computing #Domain Adaptation and Few-Shot Learning #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC)
  30. Geometric landscape annealing as an optimization principle underlying the coherent Ising machine
    2023/09/15 by Yamamura, Atsushi, Mabuchi, Hideo, Ganguli, Surya · 5 citations
    #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Optics (physics.optics)
  31. Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning
    2025/02/11 by Feng Chen, Allan Raventós, Chen, Feng +7 · 8 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computability, Logic, AI Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Parallel Computing and Optimization Techniques
  32. Task-Driven Convolutional Recurrent Models of the Visual System
    2018/06/20 by Nayebi, Aran, Bear, Daniel, Kubilius, Jonas +5 · 2 citations
    #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC)
  33. Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
    2022/10/06 by Paul, Mansheej, Chen, Feng, Larsen, Brett W. +3 · 3 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  34. Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
    2019/06/25 by Maheswaranathan, Niru, Williams, Alex, Golub, Matthew D. +2 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  35. Geometric Dynamics of Signal Propagation Predict Trainability of Transformers
    2024/03/05 by Aditya Cowsik, Cowsik, Aditya, Tamra Nebabu +5 · 3 citations
    Computer Science · Engineering · #Advanced Signal Processing Techniques #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Wireless Signal Modulation Classification
  36. Analyzing noise in autoencoders and deep networks
    2014/06/06 by Poole, Ben, Sohl-Dickstein, Jascha, Ganguli, Surya · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)
  37. Continual Learning Through Synaptic Intelligence
    2017/03/13 by Zenke, Friedemann, Poole, Ben, Ganguli, Surya · 1 citation
    #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neurons and Cognition (q-bio.NC)
  38. Understanding Self-supervised Learning with Dual Deep Networks
    2020/10/01 by Yuandong Tian, Lantao Yu, Tian, Yuandong +5 · 1 citation
    Computer Science · #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multimodal Machine Learning Applications #Topic Modeling
  39. Survey of Expressivity in Deep Neural Networks
    2016/11/24 by Maithra Raghu, Ben Poole, Raghu, Maithra +7 · 1 voice
    Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #cs.LG #cs.NE #stat.ML
  40. Variational Walkback: Learning a Transition Operator as a Stochastic\n Recurrent Net
    2017/11/06 by Anirudh Goyal, Goyal, Anirudh, Nan Rosemary Ke +5 · 1 citation
    Computer Science · Physics and Astronomy · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)
  41. Features are fate: a theory of transfer learning in high-dimensional regression
    2024/10/10 by Javan Tahir, Tahir, Javan, Surya Ganguli +3 · 2 citations
    Computer Science · #Neural Networks and Applications
  42. What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries
    2022/10/11 by Stanislav Fort, Fort, Stanislav, Ekin D. Cubuk +5 · 1 citation
    Computer Science · Engineering · #AI in cancer detection #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Imaging and Analysis
  43. Fooling LLM graders into giving better grades through neural activity guided adversarial prompting
    2024/12/17 by Atsushi Yamamura, Yamamura, Atsushi, Surya Ganguli +1 · 1 voice · 1 citation
    Computer Science · #Neural Networks and Applications
  44. Neural networks: from the perceptron to deep nets
    2023/04/13 by Gabrié, Marylou, Ganguli, Surya, Lucibello, Carlo +1 · 1 citation
    #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Statistical Mechanics (cond-mat.stat-mech)
  45. Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks
    2025/06/06 by Kunin, Daniel, Marchetti, Giovanni Luca, Chen, Feng +5 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  46. Random projections of random manifolds
    2016/07/14 by Subhaneil Lahiri, Lahiri, Subhaneil, Peiran Gao +3 · 1 citation
    Computer Science · #Topological and Geometric Data Analysis #Advanced Image and Video Retrieval Techniques #Computational Geometry and Mesh Generation