Neural Architecture Search (NAS) is a promising paradigm in automatic architecture engineering.

Furthermore, the theoretical guarantees of the transitivity and aggregation error bound are justified.

Perceiving and manipulating 3D articulated objects in diverse environments is essential for home-assistant robots.

Evidential Deep Learning (EDL), grounded in Evidence Theory and Subjective Logic (SL), provides a robust framework to estimate uncertainty for out-of-distribution (OOD) detection alongside traditional classification probabilities.

Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development.