4 accepted papers
Estimating single-cell responses across various perturbations facilitates the identification of key genes and enhances drug screening, significantly boosting experimental efficiency. However, single-cell sequencing is a destructive process, making it impossible to capture the same cell's phenotype b…
This paper introduces the Functionality-Driven Multi-Agent Group Relative Policy Optimization (FD-MAGRPO) algorithm, which is designed to enhance exploration efficiency in reinforcement learning (RL) for analog integrated circuit sizing. Our proposed method integrates two key innovations: (1) a crit
Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information. The central challenge in this field lies in balancing preconditioning effectiveness with computational efficiency of implementing the preconditioner. Among…
Deep neural networks often generalize well despite heavy over-parameterization, challenging classical parameter-based analyses. We study generalization from a representation-centric perspective and analyze how the geometry of learned embeddings controls predictive performance for a fixed trained mod…