5 papers · 1 filter
Vicinity-Guided Discriminative Latent Diffusion for Privacy-Preserving Domain Adaptation
Jing Wang, Wonho Bae, Jiahong Chen +2
Recent work on latent diffusion models (LDMs) has focused almost exclusively on generative tasks, leaving their potential for discriminative transfer largely unexplored. We introdu…
AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training
Zhenyu Han, Ansheng You, Haibo Wang +16
Reinforcement learning (RL) has become a pivotal technology in the post-training phase of large language models (LLMs). Traditional task-collocated RL frameworks suffer from signif…
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
Jing Wang, Anna Choromanska
As data sets grow in size and complexity, it is becoming more difficult to pull useful features from them using hand-crafted feature extractors. For this reason, deep learning (DL)…
Adjacent Leader Decentralized Stochastic Gradient Descent
Haoze He, Jing Wang, Anna Choromanska
This work focuses on the decentralized deep learning optimization framework. We propose Adjacent Leader Decentralized Gradient Descent (AL-DSGD), for improving final model performa…
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
Wonho Bae, Jing Wang, Danica J. Sutherland
Most meta-learning methods assume that the (very small) context set used to establish a new task at test time is passively provided. In some settings, however, it is feasible to ac…