2 citations · 2 across the 6 of their papers we have counts for
11 papers
Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch
Mary Letey, Yue M. Lu, Cengiz Pehlevan +1
Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt. Understanding how this ability…
A Random Matrix Theory Perspective on the Consistency of Diffusion Models
Binxu Wang, Jacob Zavatone-Veth, Cengiz Pehlevan
Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise seed. We trace this consistency to a…
Stationary covariance spectra of discrete-time non-normal random recurrent dynamics
Jacob A. Zavatone-Veth
Principal component analysis is widely used to characterize structure in the dynamics of recurrent neural networks. For stationary noise-driven dynamics, the distribution of varian…
Dynamics of learning to integrate in linear recurrent neural networks
Blake Bordelon, Jordan Cotler, Cengiz Pehlevan +1
Learning recurrent connectivity that supports memory over long intrinsic timescales is a basic problem in the theory of dynamical computation. While continuous attractor and integr…
Risk and cross validation in ridge regression with correlated samples
Alexander Atanasov, Jacob A. Zavatone-Veth, Cengiz Pehlevan
Recent years have seen substantial advances in our understanding of high-dimensional ridge regression, but existing theories assume that training examples are independent. By lever…
Stimulus symmetries can confound representational similarity analyses
Farhad Pashakhanloo, Jacob A. Zavatone-Veth
What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the need for a more complete char…