B. Bozkurt, A. Isfendiyaroglu, C. Pehlevan, and A. T. Erdogan, “Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation,” International Conference on Learning Representations (ICLR), 2023. PDF

B. Bordelon and C. Pehlevan, “The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks,” International Conference on Learning Representations (ICLR) (notable-top-25%), 2023. PDF

D. Lipshutz, C. Pehlevan, and D. B. Chklovskii, “Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation,” International Conference on Learning Representations (ICLR), 2023. PDF

J. A. Zavatone-Veth and C. Pehlevan, “Replica method for eigenvalues of real Wishart product matrices,” SciPost Phys. Core, vol. 6, no. 2, pp. 026, 2023. PDF

A. Canatar, E. Peters, C. Pehlevan, S. M. Wild, and R. Shaydulin, “Bandwidth Enables Generalization in Quantum Kernel Models,” Transactions on Machine Learning Research, 2023. PDF

A. Lin, et al., “Functional imaging and quantification of multi-neuronal olfactory responses in C. elegans,” Science Advances , vol. 9, no. 9, pp. eade1249, 2023. PDF

B. Bordelon and C. Pehlevan, “Population codes enable learning from few examples by shaping inductive bias,” eLife , vol. 11, pp. e78606, 2022. PDF

J. A. Zavatone-Veth*, J. A. Rubinfien*, and C. Pehlevan, “Training shapes the curvature of shallow neural network representations,” in NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022. PDF

M. Farrell*, B. Bordelon*, S. Trivedi, and C. Pehlevan, “Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?” in NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations (Oral), 2022. PDF

B. Bordelon and C. Pehlevan, “Dynamical Mean Field Theory of Kernel Evolution in Wide Neural Networks,” in NeurIPS 2022 workshop on Machine Learning and the Physical Sciences, 2022. PDF

J. A. Zavatone-Veth, W. Tong, and C. Pehlevan, “Contrasting random and learned features in deep Bayesian linear regression,” in NeurIPS 2022 workshop on Machine Learning and the Physical Sciences, 2022. PDF

A. Canatar and C. Pehlevan, “A Kernel Analysis of Feature Learning in Deep Neural Networks,” 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton). 2022. PDF

B. Bozkurt, C. Pehlevan, and A. Erdogan, “Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources,” in Advances in Neural Information Processing Systems (NeurIPS) (Oral), 2022. PDF

J. A. Zavatone-Veth, A. Canatar, B. S. Ruben, and C. Pehlevan, “Asymptotics of representation learning in finite Bayesian neural networks,” J Stat Mech: Theory and Experiments , pp. 114008, 2022. PDF

P. Masset*, J. A. Zavatone-Veth*, P. J. Connor, V. Murthy#, and C. Pehlevan#, “Natural gradient enables fast sampling in spiking neural networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2022. PDF

B. Bordelon and C. Pehlevan, “Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2022. PDF

D. Lipshutz, C. Pehlevan, and D. Chklovskii, “Biologically plausible single-layer networks for nonnegative independent component analysis,” Biological Cybernetics , 2022. PDF

J. A. Zavatone-Veth, W. L. Tong, and C. Pehlevan, “Contrasting random and learned features in deep Bayesian linear regression,” Physical Review E, vol. 105, no. 6, pp. 064118, 2022. PDF

J. A. Zavatone-Veth and C. Pehlevan, “On neural network kernels and the storage capacity problem,” Neural Computation, vol. 34, no. 5, pp. 1136-1142, 2022. PDF

A. Atanasov*, B. Bordelon*, and C. Pehlevan, “Neural Networks as Kernel Learners: The Silent Alignment Effect,” in International Conference on Learning Representations (ICLR), 2022. PDF