Deep Learning
ColJailBreak: Collaborative Generation and Editing for Jailbreaking Text-to-Image Deep Generation Yizhuo Ma
Warning: This paper contains model outputs that are offensive in nature.
Training Code Language Models with Comprehensive Semantics Reasoning
Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to bridge the gap between Code LLMs' reliance on static text data
ETO: Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses
During the coarse matching phase, we organize multiple homography hypotheses to approximate continuous matches. Each hypothesis encompasses several features to be matched, significantly reducing the number of features that require enhancement via transformers.
Simulation-Free Training of Neural ODEs on Paired Data
In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data.
SGD vs GD: Rank Deficiency in Linear Networks
Deep neural networks have significantly advanced machine learning in recent decades.
Active, anytime-valid risk controlling prediction sets
We also describe how to use predictors (i.e., the
