Genre
Transfer Learning for Diffusion Models
However, their success heavily relies on the availability of a large number of training samples.
Unchosen Experts Can Contribute Too: Unleashing MoE Models ' Power by Self-Contrast
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency.
Aligning LLM Agents by Learning Latent Preference from User Edits Ge Gao Alexey T aymanov Eduardo Salinas Paul Mineiro Dipendra Misra Department of Computer Science, Cornell University
The inferred user preference descriptions are used to define prompts for generating responses in the future.
AED: Adaptable Error Detection for Few-shot Imitation Policy Jia-Fong Y eh 1 Kuo-Han Hung 1, Pang-Chi Lo1, Chi-Ming Chung 1
We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel environments. The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios.
dattri: A Library for Efficient Data Attribution Junwei Deng 1* Ting-Wei Li
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models.
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Building on this, our study investigates the interplay between sharpness and diversity within deep ensembles, illustrating their crucial role in robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data.