5 accepted papers
Diffusion models have achieved outstanding success in image generation, yet their objectives are often limited to reconstruction, making it difficult to align with human preferences directly. Reinforcement learning (RL) offers a promising approach to address this by optimizing models using explicit
Achieving an optimal balance between exploration and exploitation remains a fundamental challenge in reinforcement learning. This work revisits the exploration-exploitation dilemma through the lens of entropy, offering a novel perspective on this enduring problem. It establishes a theoretical connec
Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive perform
Contrastive learning (CL) has been widely used to improve recommendation performance, since its self-supervised signals can effectively alleviate the data sparsity issue in recommender systems. Nevertheless, most existing CL-based recommendation models construct negative sample pairs following the c…
Face aging is of great importance for cross-age recognition and entertainment related applications. However, the lack of labeled faces of the same person across a long age range makes it challenging. Because of different aging speed of different persons, our face aging approach aims at synthesizing…