5 citations · 6 across the 11 of their papers we have counts for
5 papers · 1 filter
Understanding Factual Recall in Transformers via Associative Memories
Eshaan Nichani, Jason D. Lee, Alberto Bietti
Large language models have demonstrated an impressive ability to perform factual recall. Prior work has found that transformers trained on factual recall tasks can store informatio…
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
Hengyu Fu, Zihao Wang, Eshaan Nichani +1
In deep learning theory, a critical question is to understand how neural networks learn hierarchical features. In this work, we study the learning of hierarchical polynomials of \t…
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
Jason D. Lee, Kazusato Oko, Taiji Suzuki +1
We study the problem of gradient descent learning of a single-index target function unde…
Computational-Statistical Gaps in Gaussian Single-Index Models
Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee +1
Single-Index Models are high-dimensional regression problems with planted structure, whereby labels depend on an unknown one-dimensional projection of the input via a generic, non-…
How Transformers Learn Causal Structure with Gradient Descent
Eshaan Nichani, Alex Damian, Jason D. Lee
The incredible success of transformers on sequence modeling tasks can be largely attributed to the self-attention mechanism, which allows information to be transferred between diff…