← Authors

Cengiz Pehlevan

43 papers · 2015–2025 · 3 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (3) πŸƒ Academic Marathon (10) 🐝 Cross-Pollinator (6) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (38) 🏠 Conference Loyalist (21) πŸ”¬ Deep Specialist (11) πŸ‘‘ Triple Crown 🀝 Dynamic Duo (21) πŸ’Ž Century Club (43) ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (121) πŸ”₯ Unstoppable (8) ❓ The Questioner (2)

Conferences

NIPS (21) ICLR (14) ICML (8)

Top co-authors

Blake Bordelon (21) Alexander Atanasov (7) Jacob Zavatone-Veth (6) Tanishq Kumar (3) Bariscan Bozkurt (3) Dmitri Chklovskii (3) Paul Masset (3) Abdulkadir Canatar (3) Samuel J. Gershman (2) Ben Ruben (2)

Keywords

neural tangent kernel (4) associative memory (4) learning curve (3) representation learning (3) neural network optimization (3) feature learning (3) dynamical mean field theory (2) infinite width limit (2) kernel methods (2) dynamical system (2) kernel regression (2) similarity matching (2) statistical learning (2) neural network (2) gaussian process (2) spiking neural network (2) bayesian neural network (2) biological plausibility (2) neural dynamics (1) manifold learning (1)

Papers

Risk and cross validation in ridge regression with correlated samples ICML 2025
Scaling Laws for Precision ICLR 2025
Do Mice Grok? Glimpses of Hidden Progress in Sensory Cortex ICLR 2025
No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions ICML 2025
Adaptive kernel predictors from feature-learning infinite limits of neural networks ICML 2025
A Model of Place Field Reorganization During Reward Maximization ICML 2025
Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer ICML 2025
MLPs Learn In-Context on Regression and Classification Tasks ICLR 2025
The Optimization Landscape of SGD Across the Feature Learning Strength ICLR 2025
How Feature Learning Can Improve Neural Scaling Laws ICLR 2025
A Dynamical Model of Neural Scaling Laws ICML 2024
Grokking as the transition from lazy to rich training dynamics ICLR 2024
Infinite Limits of Multi-head Transformer Dynamics NIPS 2024
Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics NIPS 2024
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit ICLR 2024
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles NIPS 2023
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales NIPS 2023
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks NIPS 2023
Loss Dynamics of Temporal Difference Reinforcement Learning NIPS 2023
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry NIPS 2023
Learning Curves for Deep Structured Gaussian Feature Models NIPS 2023
Long Sequence Hopfield Memory NIPS 2023
Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb NIPS 2023
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes ICLR 2023
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation ICLR 2023
Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation ICLR 2023
The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks ICLR 2023
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? ICLR 2022
Neural Networks as Kernel Learners: The Silent Alignment Effect ICLR 2022
Learning Curves for SGD on Structured Features ICLR 2022
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks NIPS 2022
Natural gradient enables fast sampling in spiking neural networks NIPS 2022
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources NIPS 2022
Asymptotics of representation learning in finite Bayesian neural networks NIPS 2021
Attention Approximates Sparse Distributed Memory NIPS 2021
Out-of-Distribution Generalization in Kernel Regression NIPS 2021
Exact marginal prior distributions of finite Bayesian neural networks NIPS 2021
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders ICML 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks ICML 2020
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons NIPS 2020
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks NIPS 2019
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks NIPS 2018
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks NIPS 2015