3 accepted papers
We study the convergence rate of learning pairwise interactions in single-layer attention-style models, where tokens interact through a weight matrix and a non-linear activation function. We prove that the minimax rate is $M^{-\frac{2\beta}{2\beta+1}}$ with $M$ being the sample size, depending only…
In the field of task planning for service robots, large language model (LLM)-based approaches have shown increasing potential but still struggle with responding to complex user demands and grounded task planning. In this letter, we propose a user-demand-oriented adaptive grounded task planning syste
Despite recent popularity of attention-based neural architectures in core AI fields like natural language processing (NLP) and computer vision (CV), their potential in modeling complex physical systems remains under-explored. Learning problems in physical systems are often characterized as discoveri…