6 citations · 13 across the 11 of their papers we have counts for
12 papers
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…
Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking
Chen-Hao Chao, Wei-Fang Sun, Hanwen Liang +2
Masked diffusion models (MDM) are powerful generative models for discrete data that generate samples by progressively unmasking tokens in a sequence. Each token can take one of two…
Retraining-Free Merging of Sparse MoE via Hierarchical Clustering
I-Chun Chen, Hsu-Shen Liu, Wei-Fang Sun +3
Sparse Mixture-of-Experts (SMoE) models represent a significant advancement in large language model (LLM) development through their efficient parameter utilization. These models ac…
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…
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
Wei-Fang Sun, Cheng-Kuang Lee, Simon See +1
In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously chan…
Training Energy-Based Normalizing Flow with Score-Matching Objectives
Chen-Hao Chao, Wei-Fang Sun, Yen-Chang Hsu +2
In this paper, we establish a connection between the parameterization of flow-based and energy-based generative models, and present a new flow-based modeling approach called energy…
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
Ting-Hsuan Liao, Huang-Ru Liao, Shan-Ya Yang +8
Many unsupervised domain adaptation (UDA) methods have been proposed to bridge the domain gap by utilizing domain invariant information. Most approaches have chosen depth as such i…
On Investigating the Conservative Property of Score-Based Generative Models
Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng +1
Existing Score-Based Models (SBMs) can be categorized into constrained SBMs (CSBMs) or unconstrained SBMs (USBMs) according to their parameterization approaches. CSBMs model probab…
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng +6
Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods f…
Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encoding
Chin-Jui Chang, Chun-Yi Lee, Yi-Hsuan Yang
This paper proposes a new self-attention based model for music score infilling, i.e., to generate a polyphonic music sequence that fills in the gap between given past and future co…