1 citations · 1 across the 5 of their papers we have counts for
7 papers
Neural Constitutive Learning for Generalized Reaction-Diffusion Systems
Shang-Ke Chen, Yu-Peng Wang, Shih-Hsuan Hung +4
Generalized reaction-diffusion systems encompass diverse transport mechanisms and coupled reaction kinetics. A central question for neural PDE solvers is what should be learned so…
G3AR: Graph-Guided Neural Visual Geometry for Scalable Multi-Sequence Aerial Registration
Jeng Wen Joshua Lean, Ting-Yu Yen, Wei-Fang Sun +3
Full-context neural visual geometry is impractical for thousands of images, while sequence-based chunking poorly captures irregular non-local overlap in multi-sequence aerial colle…
VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning
Kuan-Chen Chen, Winston Chen, Wei-Fang Sun +1
Designing effective reward functions remains a major challenge in reinforcement learning (RL), particularly in open-ended environments where task goals are abstract and difficult t…
MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Scaling of Diffusion Language Models
Chen-Hao Chao, Wei-Fang Sun, Junwei Quan +2
Masked diffusion models (MDM) exhibit superior generalization when learned using a Partial masking scheme (Prime). This approach converts tokens into sub-tokens and models the diff…
Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow
Chen-Hao Chao, Chien Feng, Wei-Fang Sun +3
Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous action spaces are typically formulated based on actor-critic frameworks and optimized through a…
DriveEnv-NeRF: Exploration of A NeRF-Based Autonomous Driving Environment for Real-World Performance Validation
Mu-Yi Shen, Chia-Chi Hsu, Hao-Yu Hou +5
In this study, we introduce the DriveEnv-NeRF framework, which leverages Neural Radiance Fields (NeRF) to enable the validation and faithful forecasting of the efficacy of autonomo…
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun +3
In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where acquiring multiple expert demonstrations is costly o…