Statistical Learning
Collaborative Score Distillation for Consistent Visual Editing
We start from an intuition that many complex visual data, e.g., videos and 3D scenes, are represented
Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots Ruixiang T ang
In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples.
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision (Appendix)
We make the comparisons of different NAS methods in terms of the empirical running time.
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
We propose Any2Graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i.e. a deep learning model that predicts an entire graph for any kind of input.
Multi-Scale Representation Learning for Protein Fitness Prediction
Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets.