5 accepted papers
Long-tailed semi-supervised learning is hampered by a vicious cycle of confirmation bias, where skewed pseudo-labeling progressively marginalizes tail classes. This challenge is compounded in real-world scenarios by a class distribution mismatch between labeled and unlabeled data, rendering the bias…
Large Language Models (LLMs) require high quality preference datasets to align with human preferences. However, conventional methods for constructing such datasets face significant challenges: reliance on pre-collected instructions often leads to distribution mismatches with target models, while the
To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity. The ineffectiveness comes from the conflict between the fact of asymmetric and multi-modal distribution of likely outcomes and symmetric and single-modal original distribution and superv…
Autonomous soft tissue manipulation in robotic surgery remains challenging. Modeling the tool-tissue interaction, analyzing the tissue structural deformation and monitoring the biomechanical status during surgery manipulation may benefit the development of autonomous surgery; however, there are curr
Multi-robot systems have been shown to build large-scale, user-specified structures using distributed, environmentally-mediated coordination in simulation. Little attention, however, has been devoted to error propagation and mitigation. In this paper, we introduce a detailed simulation of TERMES, a…