Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature. In this paper, we tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences, considering both rationality and functionality.

These methods can be broadly categorized into two types: function learning and operator learning approaches. In function learning, the goal is to directly learn the solution.

Recently powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide.

Full experimental details are provided in Appendix C.

In label-noise learning, the noise transition matrix reveals how an instance transitions from its clean label to its noisy label. Accurately estimating an instance's noise transition matrix is crucial for estimating its clean label.

This study proposes a novel framework, Event-based Graph Spatiotemporal Sensitive Transformer (EGSST), for the exploitation of spatial and temporal properties of event data.

High-quality public datasets significantly prompt the prosperity of deep neural networks (DNNs).

CF with the same training epochs in both clean and poisoned data contexts.

Due to the dynamic differences of different samples, our CCF employs specific fusion constraints for each individual in practice.