B. Bordelon and C. Pehlevan, “Learning Curves for SGD on Structured Features,” arXiv preprint arXiv:2106.02713, 2021.
A. Canatar, B. Bordelon, and C. Pehlevan, “Out-of-Distribution Generalization in Kernel Regression,” arXiv preprint arXiv:2106.02261, 2021.
J. A. Zavatone-Veth, A. Canatar, and C. Pehlevan, “Asymptotics of representation learning in finite Bayesian neural networks ,” arXiv preprint arXiv:2106.00651, 2021.
J. A. Zavatone-Veth and C. Pehlevan, “Exact priors of finite neural networks,” arXiv preprint arXiv:2104.11734, 2021.
B. Bordelon and C. Pehlevan, “Population Codes Enable Learning from Few Examples By Shaping Inductive Bias,” biorXiv 10.1101/2021.03.30.437743v1, 2021.
J. Zavatone-Veth and C. Pehlevan, “Activation function dependence of the storage capacity of treelike neural networks,” Physical Review E, vol. 103, pp. L020301, 2021. PDF
A. Canatar, B. Bordelon, and C. Pehlevan, “Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks,” Nature Communications, vol. 12, pp. 2914 , 2021. PDF
K. Vogt, et al., “Internal state configures olfactory behavior and early sensory processing in Drosophila larva,” Science Advances, vol. 7, no. 1, pp. eabd6900, 2021. PDF
S. Qin, N. Mudur, and C. Pehlevan, “Contrastive Similarity Matching for Supervised Learning,” Neural Computation, vol. 33, no. 5, pp. 1300–1328, 2021. PDF
D. Obeid, J. Zavatone-Veth, and C. Pehlevan, “Statistical structure of the trial-to-trial timing variability in synfire chains,” Physical Review E, vol. 102, no. 5, pp. 052406, 2020. PDF
Q. Li and C. Pehlevan, “Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons,” in Advances in Neural Information Processing Systems (NeurIPS), 2020. PDF
B. Bordelon, A. Canatar, and C. Pehlevan, “Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks,” International Conference of Machine Learning (ICML), 2020. PDF
Y. Jiang and C. Pehlevan, “Associative Memory in Iterated Overparameterized Sigmoid Autoencoders,” International Conference on Machine Learning (ICML), 2020. PDF
C. Pehlevan, X. Zhao, A. Sengupta, and D. B. Chklovskii, “Neurons as canonical correlation analyzers,” Frontiers in Computational Neuroscience, vol. 14, pp. 55, 2020. PDF
J. Grimaud, W. Dorrell, C. Pehlevan, and V. Murthy, “Bilateral alignment of receptive fields in the olfactory cortex points to non-random connectivity,” bioRxiv, 2020.
A. Erdogan and C. Pehlevan, “Blind Bounded Source Separation Using Neural Networks with Local Learning Rules,” in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.
H. Sikka*, W. Zhong*, J. Yin, and C. Pehlevan, “A closer look at disentangling in β-VAE,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019.
D. Obeid, H. Ramambason, and C. Pehlevan, “Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019. PDF
C. Pehlevan and D. B. Chklovskii, “Neuroscience-inspired online unsupervised learning algorithms,” IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 88-96, 2019.
C. Pehlevan, “A Spiking Neural Network with Local Learning Rules Derived From Nonnegative Similarity Matching,” in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.