3 papers
stat.ML2026
Multi-layer Cross-attention is Provably Optimal for Multi-modal In-context Learning
Nicholas Barnfield, Subhabrata Sen, Pragya Sur
Recent progress has rapidly advanced our understanding of the mechanisms underlying in-context learning in modern attention-based neural networks. However, existing results focus e…
stat.ML2026
Sharp Capacity Thresholds in Linear Associative Memory: From Top-1 Retrieval to Tail-Average Learning
Nicholas Barnfield, Juno Kim, Eshaan Nichani +2
How many key-value associations can a linear memory store? The answer depends not only on the degrees of freedom in the memory matrix, but also on the retrieval c…
cs.LG2025
High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification
Nicholas Barnfield, Hugo Cui, Yue M. Lu
When and how can an attention mechanism learn to selectively attend to informative tokens, thereby enabling detection of weak, rare, and sparsely located features? We address these…