Cengiz Pehlevan

Alexandru Meterez

James B. Simon

CoRR, 2024

A Brain-Inspired Regularizer for Adversarial Robustness.

[BibT_eX]

[DOI]

Elie Attias

Dina Obeid

CoRR, 2024

How Feature Learning Can Improve Neural Scaling Laws.

[BibT_eX]

[DOI]

CoRR, 2024

Risk and cross validation in ridge regression with correlated samples.

[BibT_eX]

[DOI]

CoRR, 2024

Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space.

[BibT_eX]

[DOI]

Sheng Yang

Peihan Liu

CoRR, 2024

Spectral regularization for adversarially-robust representation learning.

[BibT_eX]

[DOI]

Sheng Yang

CoRR, 2024

Infinite Limits of Multi-head Transformer Dynamics.

[BibT_eX]

[DOI]

Hamza Tahir Chaudhry

CoRR, 2024

MLPs Learn In-Context.

[BibT_eX]

[DOI]

William L. Tong

CoRR, 2024

Asymptotic theory of in-context learning by linear attention.

[BibT_eX]

[DOI]

Yue M. Lu

Mary I. Letey

Anindita Maiti

CoRR, 2024

Scaling and renormalization in high-dimensional regression.

[BibT_eX]

[DOI]

CoRR, 2024

A Dynamical Model of Neural Scaling Laws.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Grokking as the transition from lazy to rich training dynamics.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Bandwidth Enables Generalization in Quantum Kernel Models.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Dynamics of Temporal Difference Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Learning Curves for Heterogeneous Feature-Subsampled Ridge Ensembles.

[BibT_eX]

[DOI]

Benjamin S. Ruben

CoRR, 2023

Neural networks learn to magnify areas near decision boundaries.

[BibT_eX]

[DOI]

Sheng Yang

Julian A. Rubinfien

CoRR, 2023

Learning Curves for Deep Structured Gaussian Feature Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles.

[BibT_eX]

[DOI]

Benjamin S. Ruben

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Long Sequence Hopfield Memory.

[BibT_eX]

[DOI]

Hamza Tahir Chaudhry

Dmitry Krotov

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry.

[BibT_eX]

[DOI]

Bariscan Bozkurt

Alper T. Erdogan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Loss Dynamics of Temporal Difference Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Feature-Learning Networks Are Consistent Across Widths At Realistic Scales.

[BibT_eX]

[DOI]

Nikhil Vyas

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation.

[BibT_eX]

[DOI]

David Lipshutz

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes.

[BibT_eX]

[DOI]

Sabarish Sainathan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

On Neural Network Kernels and the Storage Capacity Problem.

[BibT_eX]

[DOI]

Neural Comput., 2022

Contrasting random and learned features in deep Bayesian linear regression.

[BibT_eX]

[DOI]

William L. Tong

CoRR, 2022

Biologically plausible single-layer networks for nonnegative independent component analysis.

[BibT_eX]

[DOI]

David Lipshutz

Biol. Cybern., 2022

Natural gradient enables fast sampling in spiking neural networks.

[BibT_eX]

[DOI]

Paul Masset

J. Patrick Connor

Venkatesh Murthy

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources.

[BibT_eX]

[DOI]

Bariscan Bozkurt

Alper T. Erdogan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Curves for SGD on Structured Features.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Neural Networks as Kernel Learners: The Silent Alignment Effect.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

A Kernel Analysis of Feature Learning in Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 58th Annual Allerton Conference on Communication, 2022

2021

Contrastive Similarity Matching for Supervised Learning.

[BibT_eX]

[DOI]

Shanshan Qin

Nayantara Mudur

Neural Comput., 2021

Asymptotics of representation learning in finite Bayesian neural networks.

[BibT_eX]

[DOI]

CoRR, 2021

Exact priors of finite neural networks.

[BibT_eX]

[DOI]

CoRR, 2021

Exact marginal prior distributions of finite Bayesian neural networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Asymptotics of representation learning in finite Bayesian neural networks.

[BibT_eX]

[DOI]

Benjamin S. Ruben

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Out-of-Distribution Generalization in Kernel Regression.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Attention Approximates Sparse Distributed Memory.

[BibT_eX]

[DOI]

Trenton Bricken

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Depth induces scale-averaging in overparameterized linear Bayesian neural networks.

[BibT_eX]

[DOI]

Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020

Neurons as Canonical Correlation Analyzers.

[BibT_eX]

[DOI]

Frontiers Comput. Neurosci., 2020

Activation function dependence of the storage capacity of treelike neural networks.

[BibT_eX]

[DOI]

CoRR, 2020

Statistical Mechanics of Generalization in Kernel Regression.

[BibT_eX]

[DOI]

CoRR, 2020

Supervised Deep Similarity Matching.

[BibT_eX]

[DOI]

Shanshan Qin

Nayantara Mudur

CoRR, 2020

Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons.

[BibT_eX]

[DOI]

Qianyi Li

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Associative Memory in Iterated Overparameterized Sigmoid Autoencoders.

[BibT_eX]

[DOI]

Yibo Jiang

Proceedings of the 37th International Conference on Machine Learning, 2020

Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Blind Bounded Source Separation Using Neural Networks with Local Learning Rules.

[BibT_eX]

[DOI]

Alper T. Erdogan

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Neuroscience-Inspired Online Unsupervised Learning Algorithms: Artificial neural networks.

[BibT_eX]

[DOI]

IEEE Signal Process. Mag., 2019

Neuroscience-inspired online unsupervised learning algorithms.

[BibT_eX]

[DOI]

CoRR, 2019

Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks.

[BibT_eX]

[DOI]

Dina Obeid

Hugo Ramambason

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Spiking Neural Network with Local Learning Rules Derived from Nonnegative Similarity Matching.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2019

A Closer Look at Disentangling in β-VAE.

[BibT_eX]

[DOI]

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks?

[BibT_eX]

[DOI]

Anirvan M. Sengupta

Neural Comput., 2018

Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics.

[BibT_eX]

[DOI]

Victor Minden

Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017

Blind Nonnegative Source Separation Using Biological Neural Networks.

[BibT_eX]

[DOI]

Sreyas Mohan

Neural Comput., 2017

Adversarial synapses: Hebbian/anti-Hebbian learning optimizes min-max objectives.

[BibT_eX]

[DOI]

Anirvan M. Sengupta

CoRR, 2017

Resource-efficient perceptron has sparse synaptic weight distribution.

[BibT_eX]

[DOI]

Anirvan M. Sengupta

Proceedings of the 25th Signal Processing and Communications Applications Conference, 2017

A clustering neural network model of insect olfaction.

[BibT_eX]

[DOI]

Alexander Genkin

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

Self-calibrating neural networks for dimensionality reduction.

[BibT_eX]

[DOI]

Yuansi Chen

Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?

[BibT_eX]

[DOI]

Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

2015

A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data.

[BibT_eX]

[DOI]

Tao Hu

Neural Comput., 2015

A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening.

[BibT_eX]

[DOI]

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

2014

A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features.

[BibT_eX]

[DOI]

Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

A Hebbian/Anti-Hebbian network for online sparse dictionary learning derived from symmetric matrix factorization.

[BibT_eX]

[DOI]

Tao Hu