JADE is a unified framework that simultaneously learns spot-wise alignments and shared low-dimensional embeddings to robustly integrate multi-slice spatial transcriptomics data.

A synthesis of layer-wise interventions, empirical experiments, and previous research suggests that inference in decoder-only LLMs unfolds in distinct phases.

This paper explores gaze prediction and fixation modeling in medical images using deep learning techniques.

We propose a unified optimization objective and thus derive a novel interpretable and efficient self-attention mechanism, named Contract-and-Broadcast Self-Attention (CBSA).