3 citations · 8 across the 5 of their papers we have counts for
5 papers
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning
Han Zhou, Xingchen Wan, Ivan Vulić +1
Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has received…
Better Zero-Shot Reasoning with Self-Adaptive Prompting
Xingchen Wan, Ruoxi Sun, Hanjun Dai +2
Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible…
SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning
Xinghui Li, Kai Han, Xingchen Wan +1
We propose SimSC, a remarkably simple framework, to address the problem of semantic matching only based on the feature backbone. We discover that when fine-tuning ImageNet pre-trai…
Bayesian Quadrature for Neural Ensemble Search
Saad Hamid, Xingchen Wan, Martin Jørgensen +2
Ensembling can improve the performance of Neural Networks, but existing approaches struggle when the architecture likelihood surface has dispersed, narrow peaks. Furthermore, exist…
Bayesian Generational Population-Based Training
Xingchen Wan, Cong Lu, Jack Parker-Holder +4
Reinforcement learning (RL) offers the potential for training generally capable agents that can interact autonomously in the real world. However, one key limitation is the brittlen…