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2020 – today
- 2024
[c46]Blake Bordelon, Lorenzo Noci, Mufan Bill Li, Boris Hanin, Cengiz Pehlevan:
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit. ICLR 2024- [c45]Tanishq Kumar, Blake Bordelon, Samuel J. Gershman, Cengiz Pehlevan:
Grokking as the transition from lazy to rich training dynamics. ICLR 2024 - [c44]Blake Bordelon, Alexander B. Atanasov, Cengiz Pehlevan:
A Dynamical Model of Neural Scaling Laws. ICML 2024 - [i55]Blake Bordelon, Alexander B. Atanasov, Cengiz Pehlevan
:
A Dynamical Model of Neural Scaling Laws. CoRR abs/2402.01092 (2024) - [i54]Alexander B. Atanasov, Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Scaling and renormalization in high-dimensional regression. CoRR abs/2405.00592 (2024) - [i53]Yue M. Lu, Mary I. Letey, Jacob A. Zavatone-Veth, Anindita Maiti, Cengiz Pehlevan:
Asymptotic theory of in-context learning by linear attention. CoRR abs/2405.11751 (2024) - [i52]William L. Tong, Cengiz Pehlevan:
MLPs Learn In-Context. CoRR abs/2405.15618 (2024) - [i51]Blake Bordelon, Hamza Tahir Chaudhry, Cengiz Pehlevan:
Infinite Limits of Multi-head Transformer Dynamics. CoRR abs/2405.15712 (2024) - [i50]Sheng Yang, Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Spectral regularization for adversarially-robust representation learning. CoRR abs/2405.17181 (2024) - [i49]Sheng Yang, Peihan Liu, Cengiz Pehlevan:
Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space. CoRR abs/2405.17198 (2024) - [i48]Alexander B. Atanasov, Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Risk and cross validation in ridge regression with correlated samples. CoRR abs/2408.04607 (2024) - [i47]Blake Bordelon, Alexander B. Atanasov, Cengiz Pehlevan:
How Feature Learning Can Improve Neural Scaling Laws. CoRR abs/2409.17858 (2024) - 2023
- [j9]Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan, Stefan M. Wild, Ruslan Shaydulin:
Bandwidth Enables Generalization in Quantum Kernel Models. Trans. Mach. Learn. Res. 2023 (2023) - [c43]Alexander B. Atanasov, Blake Bordelon, Sabarish Sainathan, Cengiz Pehlevan:
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. ICLR 2023 - [c42]Blake Bordelon, Cengiz Pehlevan:
The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks. ICLR 2023 - [c41]Bariscan Bozkurt, Ates Isfendiyaroglu, Cengiz Pehlevan, Alper Tunga Erdogan:
Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation. ICLR 2023 - [c40]David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii:
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation. ICLR 2023 - [c39]Nikhil Vyas, Alexander B. Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan, Cengiz Pehlevan:
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales. NeurIPS 2023 - [c38]Blake Bordelon, Paul Masset, Henry Kuo, Cengiz Pehlevan:
Loss Dynamics of Temporal Difference Reinforcement Learning. NeurIPS 2023 - [c37]Blake Bordelon, Cengiz Pehlevan:
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks. NeurIPS 2023 - [c36]Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan:
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry. NeurIPS 2023 - [c35]Hamza Tahir Chaudhry, Jacob A. Zavatone-Veth, Dmitry Krotov, Cengiz Pehlevan:
Long Sequence Hopfield Memory. NeurIPS 2023 - [c34]Benjamin S. Ruben, Cengiz Pehlevan:
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles. NeurIPS 2023 - [c33]Jacob A. Zavatone-Veth, Paul Masset, William L. Tong, Joseph D. Zak, Venkatesh Murthy, Cengiz Pehlevan:
Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb. NeurIPS 2023 - [c32]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Learning Curves for Deep Structured Gaussian Feature Models. NeurIPS 2023 - [i46]Jacob A. Zavatone-Veth, Sheng Yang, Julian A. Rubinfien, Cengiz Pehlevan:
Neural networks learn to magnify areas near decision boundaries. CoRR abs/2301.11375 (2023) - [i45]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Learning curves for deep structured Gaussian feature models. CoRR abs/2303.00564 (2023) - [i44]Blake Bordelon, Cengiz Pehlevan:
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks. CoRR abs/2304.03408 (2023) - [i43]Nikhil Vyas, Alexander B. Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan, Cengiz Pehlevan:
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales. CoRR abs/2305.18411 (2023) - [i42]Hamza Tahir Chaudhry, Jacob A. Zavatone-Veth, Dmitry Krotov, Cengiz Pehlevan:
Long Sequence Hopfield Memory. CoRR abs/2306.04532 (2023) - [i41]Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan:
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry. CoRR abs/2306.04810 (2023) - [i40]Benjamin S. Ruben, Cengiz Pehlevan:
Learning Curves for Heterogeneous Feature-Subsampled Ridge Ensembles. CoRR abs/2307.03176 (2023) - [i39]Blake Bordelon, Paul Masset, Henry Kuo, Cengiz Pehlevan:
Dynamics of Temporal Difference Reinforcement Learning. CoRR abs/2307.04841 (2023) - [i38]Blake Bordelon, Lorenzo Noci, Mufan Bill Li, Boris Hanin, Cengiz Pehlevan:
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit. CoRR abs/2309.16620 (2023) - [i37]Tanishq Kumar, Blake Bordelon, Samuel J. Gershman, Cengiz Pehlevan:
Grokking as the Transition from Lazy to Rich Training Dynamics. CoRR abs/2310.06110 (2023) - 2022
- [j8]David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii:
Biologically plausible single-layer networks for nonnegative independent component analysis. Biol. Cybern. 116(5): 557-568 (2022) - [j7]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
On Neural Network Kernels and the Storage Capacity Problem. Neural Comput. 34(5): 1136-1142 (2022) - [c31]Abdulkadir Canatar, Cengiz Pehlevan:
A Kernel Analysis of Feature Learning in Deep Neural Networks. Allerton 2022: 1-8 - [c30]Alexander B. Atanasov, Blake Bordelon, Cengiz Pehlevan:
Neural Networks as Kernel Learners: The Silent Alignment Effect. ICLR 2022 - [c29]Blake Bordelon, Cengiz Pehlevan:
Learning Curves for SGD on Structured Features. ICLR 2022 - [c28]Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, Cengiz Pehlevan:
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? ICLR 2022 - [c27]Blake Bordelon, Cengiz Pehlevan:
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks. NeurIPS 2022 - [c26]Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan:
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources. NeurIPS 2022 - [c25]Paul Masset, Jacob A. Zavatone-Veth, J. Patrick Connor, Venkatesh Murthy, Cengiz Pehlevan:
Natural gradient enables fast sampling in spiking neural networks. NeurIPS 2022 - [i36]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
On neural network kernels and the storage capacity problem. CoRR abs/2201.04669 (2022) - [i35]Jacob A. Zavatone-Veth, William L. Tong, Cengiz Pehlevan:
Contrasting random and learned features in deep Bayesian linear regression. CoRR abs/2203.00573 (2022) - [i34]Blake Bordelon, Cengiz Pehlevan:
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks. CoRR abs/2205.09653 (2022) - [i33]Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan, Stefan M. Wild, Ruslan Shaydulin:
Bandwidth Enables Generalization in Quantum Kernel Models. CoRR abs/2206.06686 (2022) - [i32]David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii:
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation. CoRR abs/2209.10634 (2022) - [i31]Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan:
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources. CoRR abs/2209.12894 (2022) - [i30]Blake Bordelon, Cengiz Pehlevan:
The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks. CoRR abs/2210.02157 (2022) - [i29]Bariscan Bozkurt, Ates Isfendiyaroglu, Cengiz Pehlevan, Alper T. Erdogan:
Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation. CoRR abs/2210.04222 (2022) - [i28]Alexander B. Atanasov, Blake Bordelon, Sabarish Sainathan, Cengiz Pehlevan:
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. CoRR abs/2212.12147 (2022) - 2021
- [j6]Shanshan Qin, Nayantara Mudur, Cengiz Pehlevan:
Contrastive Similarity Matching for Supervised Learning. Neural Comput. 33(5): 1300-1328 (2021) - [c24]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Depth induces scale-averaging in overparameterized linear Bayesian neural networks. ACSCC 2021: 600-607 - [c23]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Exact marginal prior distributions of finite Bayesian neural networks. NeurIPS 2021: 3364-3375 - [c22]Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan:
Out-of-Distribution Generalization in Kernel Regression. NeurIPS 2021: 12600-12612 - [c21]Trenton Bricken, Cengiz Pehlevan:
Attention Approximates Sparse Distributed Memory. NeurIPS 2021: 15301-15315 - [c20]Jacob A. Zavatone-Veth, Abdulkadir Canatar, Benjamin S. Ruben, Cengiz Pehlevan:
Asymptotics of representation learning in finite Bayesian neural networks. NeurIPS 2021: 24765-24777 - [i27]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Exact priors of finite neural networks. CoRR abs/2104.11734 (2021) - [i26]Jacob A. Zavatone-Veth, Abdulkadir Canatar, Cengiz Pehlevan:
Asymptotics of representation learning in finite Bayesian neural networks. CoRR abs/2106.00651 (2021) - [i25]Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan:
Out-of-Distribution Generalization in Kernel Regression. CoRR abs/2106.02261 (2021) - [i24]Blake Bordelon, Cengiz Pehlevan:
Learning Curves for SGD on Structured Features. CoRR abs/2106.02713 (2021) - [i23]Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, Cengiz Pehlevan:
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? CoRR abs/2110.07472 (2021) - [i22]Alexander B. Atanasov, Blake Bordelon, Cengiz Pehlevan:
Neural Networks as Kernel Learners: The Silent Alignment Effect. CoRR abs/2111.00034 (2021) - [i21]Trenton Bricken, Cengiz Pehlevan:
Attention Approximates Sparse Distributed Memory. CoRR abs/2111.05498 (2021) - [i20]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Depth induces scale-averaging in overparameterized linear Bayesian neural networks. CoRR abs/2111.11954 (2021) - 2020
- [j5]Cengiz Pehlevan, Xinyuan Zhao, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Neurons as Canonical Correlation Analyzers. Frontiers Comput. Neurosci. 14: 55 (2020) - [c19]Alper T. Erdogan, Cengiz Pehlevan:
Blind Bounded Source Separation Using Neural Networks with Local Learning Rules. ICASSP 2020: 3812-3816 - [c18]Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan:
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. ICML 2020: 1024-1034 - [c17]Yibo Jiang, Cengiz Pehlevan:
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders. ICML 2020: 4828-4838 - [c16]Qianyi Li, Cengiz Pehlevan:
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons. NeurIPS 2020 - [i19]Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan:
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. CoRR abs/2002.02561 (2020) - [i18]Shanshan Qin, Nayantara Mudur, Cengiz Pehlevan:
Supervised Deep Similarity Matching. CoRR abs/2002.10378 (2020) - [i17]Alper T. Erdogan, Cengiz Pehlevan:
Blind Bounded Source Separation Using Neural Networks with Local Learning Rules. CoRR abs/2004.05479 (2020) - [i16]Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan:
Statistical Mechanics of Generalization in Kernel Regression. CoRR abs/2006.13198 (2020) - [i15]Yibo Jiang, Cengiz Pehlevan:
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders. CoRR abs/2006.16540 (2020) - [i14]Jacob A. Zavatone-Veth, Cengiz Pehlevan:
Activation function dependence of the storage capacity of treelike neural networks. CoRR abs/2007.11136 (2020)
2010 – 2019
- 2019
- [j4]Cengiz Pehlevan, Dmitri B. Chklovskii:
Neuroscience-Inspired Online Unsupervised Learning Algorithms: Artificial neural networks. IEEE Signal Process. Mag. 36(6): 88-96 (2019) - [c15]Harshvardhan Sikka, Weishun Zhong, Jun Yin, Cengiz Pehlevan:
A Closer Look at Disentangling in β-VAE. ACSSC 2019: 888-895 - [c14]Cengiz Pehlevan:
A Spiking Neural Network with Local Learning Rules Derived from Nonnegative Similarity Matching. ICASSP 2019: 7958-7962 - [c13]Dina Obeid, Hugo Ramambason, Cengiz Pehlevan:
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks. NeurIPS 2019: 15377-15386 - [i13]Cengiz Pehlevan:
A Spiking Neural Network with Local Learning Rules Derived From Nonnegative Similarity Matching. CoRR abs/1902.01429 (2019) - [i12]Cengiz Pehlevan, Dmitri B. Chklovskii:
Neuroscience-inspired online unsupervised learning algorithms. CoRR abs/1908.01867 (2019) - [i11]Dina Obeid, Hugo Ramambason, Cengiz Pehlevan:
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks. CoRR abs/1910.04958 (2019) - [i10]Harshvardhan Sikka, Weishun Zhong, Jun Yin, Cengiz Pehlevan:
A Closer Look at Disentangling in β-VAE. CoRR abs/1912.05127 (2019) - 2018
- [j3]Cengiz Pehlevan, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks? Neural Comput. 30(1) (2018) - [c12]Victor Minden, Cengiz Pehlevan, Dmitri B. Chklovskii:
Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics. ACSSC 2018: 104-111 - [c11]Andrea Giovannucci, Victor Minden, Cengiz Pehlevan, Dmitri B. Chklovskii:
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching. IEEE BigData 2018: 1015-1022 - [c10]Anirvan M. Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri B. Chklovskii:
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks. NeurIPS 2018: 7080-7090 - [i9]Andrea Giovannucci, Victor Minden, Cengiz Pehlevan, Dmitri B. Chklovskii:
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching. CoRR abs/1808.02083 (2018) - 2017
- [j2]Cengiz Pehlevan, Sreyas Mohan, Dmitri B. Chklovskii:
Blind Nonnegative Source Separation Using Biological Neural Networks. Neural Comput. 29(11) (2017) - [c9]Cengiz Pehlevan, Alexander Genkin, Dmitri B. Chklovskii:
A clustering neural network model of insect olfaction. ACSSC 2017: 593-600 - [c8]Cengiz Pehlevan, Anirvan M. Sengupta:
Resource-efficient perceptron has sparse synaptic weight distribution. SIU 2017: 1-4 - [i8]Cengiz Pehlevan, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Adversarial synapses: Hebbian/anti-Hebbian learning optimizes min-max objectives. CoRR abs/1703.07914 (2017) - [i7]Cengiz Pehlevan, Sreyas Mohan, Dmitri B. Chklovskii:
Blind nonnegative source separation using biological neural networks. CoRR abs/1706.00382 (2017) - 2016
- [c7]Yuansi Chen, Cengiz Pehlevan, Dmitri B. Chklovskii:
Self-calibrating neural networks for dimensionality reduction. ACSSC 2016: 1488-1495 - [c6]Reza Abbasi-Asl, Cengiz Pehlevan, Bin Yu, Dmitri B. Chklovskii:
Do retinal ganglion cells project natural scenes to their principal subspace and whiten them? ACSSC 2016: 1641-1645 - [i6]Yuansi Chen, Cengiz Pehlevan, Dmitri B. Chklovskii:
Self-calibrating Neural Networks for Dimensionality Reduction. CoRR abs/1612.03480 (2016) - 2015
- [j1]Cengiz Pehlevan, Tao Hu, Dmitri B. Chklovskii:
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data. Neural Comput. 27(7): 1461-1495 (2015) - [c5]Cengiz Pehlevan, Dmitri B. Chklovskii:
Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening. Allerton 2015: 1458-1465 - [c4]Cengiz Pehlevan, Dmitri B. Chklovskii:
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks. NIPS 2015: 2269-2277 - [i5]Cengiz Pehlevan, Tao Hu, Dmitri B. Chklovskii:
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data. CoRR abs/1503.00669 (2015) - [i4]Cengiz Pehlevan, Dmitri B. Chklovskii:
A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features. CoRR abs/1503.00680 (2015) - [i3]Tao Hu, Cengiz Pehlevan, Dmitri B. Chklovskii:
A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization. CoRR abs/1503.00690 (2015) - [i2]Cengiz Pehlevan, Dmitri B. Chklovskii:
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks. CoRR abs/1511.09426 (2015) - [i1]Cengiz Pehlevan, Dmitri B. Chklovskii:
Optimization theory of Hebbian/anti-Hebbian networks for PCA and whitening. CoRR abs/1511.09468 (2015) - 2014
- [c3]Tao Hu, Cengiz Pehlevan, Dmitri B. Chklovskii:
A Hebbian/Anti-Hebbian network for online sparse dictionary learning derived from symmetric matrix factorization. ACSSC 2014: 613-619 - [c2]Cengiz Pehlevan, Dmitri B. Chklovskii:
A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features. ACSSC 2014: 769-775 - 2013
- [c1]Tao Hu, Zaid J. Towfic, Cengiz Pehlevan, Alex V. Genkin, Dmitri B. Chklovskii:
A neuron as a signal processing device. ACSSC 2013: 362-366
Coauthor Index
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