Pehlevan Group

Our research areas are theoretical neuroscience and theory of neural computation in natural and artificial systems.

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Latest Publications and Preprints

J. A. Zavatone-Veth, W. L. Tong, and C. Pehlevan, “Contrasting random and learned features in deep Bayesian linear regression,” arXiv preprint arXiv:2203.00573, 2022.

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.

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

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 International Conference on Learning Representations (ICLR), 2022.

B. Bordelon and C. Pehlevan, “Learning Curves for Stochastic Gradient Descent on Structured Features,” in International Conference on Learning Representations (ICLR), 2022.

J. A. Zavatone-Veth and C. Pehlevan, “Depth induces scale-averaging in overparameterized linear Bayesian neural networks,” in 55th Asilomar Conference on Signals, Systems, and Computers, 2021.

J. A. Zavatone-Veth and C. Pehlevan, “Exact marginal prior distributions of finite Bayesian neural networks,” in Advances in Neural Information Processing Systems (NeurIPS), (Spotlight), 2021.

J. A. Zavatone-Veth, A. Canatar, B. Ruben, and C. Pehlevan, “Asymptotics of representation learning in finite Bayesian networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2021.

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