Asia
CQM: Curriculum Reinforcement Learning with a Quantized World Model
Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensional space.
Adaptive Layer Sparsity for Large Language Models via Activation Correlation Assessment Wei Li
Large Language Models (LLMs) have revolutionized the field of natural language processing with their impressive capabilities.
Bridging Geometric States via Geometric Diffusion Bridge Shengjie Luo
In this work, we introduce the Geometric Diffusion Bridge (GDB), a novel generative modeling framework that accurately bridges initial and target geometric states.
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
We introduce a new approach, Generative mOdel finetun-ing with Reward-driven Sample selection (GORS), to boost the compositional text-to-image generation abilities of pretrained text-to-image models.
Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects Alexander W. Levis
To fill this gap, we introduce a nonparametric causal inference framework for analyzing "closed-loop" designs, which use