67 citations · 588 across the 61 of their papers we have counts for
5 papers · 1 filter
Towards Instance-adaptive Inference for Federated Learning
Chun-Mei Feng, Kai Yu, Nian Liu +3
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. However, the performance of…
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
Chun-Mei Feng, Kai Yu, Yong Liu +2
Benefiting from prompt tuning, recent years have witnessed the promising performance of pre-trained vision-language models, e.g., CLIP, on versatile downstream tasks. In this paper…
Large Language Models Are Semi-Parametric Reinforcement Learning Agents
Danyang Zhang, Lu Chen, Situo Zhang +3
Inspired by the insights in cognitive science with respect to human memory and reasoning mechanism, a novel evolvable LLM-based (Large Language Model) agent framework is proposed a…
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation
Yiming Ai, Zhiwei He, Kai Yu +1
Tense inconsistency frequently occurs in machine translation. However, there are few criteria to assess the model's mastery of tense prediction from a linguistic perspective. In th…
PointGPT: Auto-regressively Generative Pre-training from Point Clouds
Guangyan Chen, Meiling Wang, Yi Yang +3
Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired…