36 papers
A Defense of the Quadratic Model
Alexandru Meterez, Pranav Ajit Nair, Depen Morwani +3
Due to the complexity of neural network loss landscapes, optimization theory is forced to rely on idealized models, and there is generally a tradeoff between how theoretically trac…
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…
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…
An Asymptotic Theory of Chain-of-Thought in In-Context Learning
Kaito Takanami, Cengiz Pehlevan
Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermediate reasoning steps at infere…
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…