8 accepted papers
Data augmentation is an essential technique in natural language processing (NLP) for enriching training datasets by generating diverse samples. This process is crucial for improving the robustness and generalization capabilities of NLP models. However, a significant challenge remains: Insufficient A…
The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus, the efficacy of machine learning (ML) algorithms is closely related to the quality of feature engineering. As one of the most important techniques, feature genera
Lower limb exoskeletons (LLEs) play a crucial role in assisting paraplegic patients with walking in outdoor environments characterized by complex terrains, including various stairs, slopes, and uneven grounds. However, most existing control methods for LLEs rely on predefined joint angles, lacking t
Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's perspective, this presents a unique challenge. If models could autonomously retrieve knowledge relevant for transfer or dec
Although MOTIP is the SOTA multi-object tracking method, there are still some issues that limit its performance. First, MOTIP still has defects in temporal information modeling, which leads to the failure to fully utilize the historical information of the tracked target and affects the correlation p…
Lower Limb Exoskeletons are potential in the gait training for patients with gait disorders. For patients in the early rehabilitation stages with weak upper limb strength, it is challenge to keep balance by themselves only. A mobile robotic walker is helpful to maintain the walking balance, with the
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM le…
In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume equal importance across all samples and features in one dataset. This simplified processing methods overlook the unique…