6 citations · 9 across the 8 of their papers we have counts for
10 papers
Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
Seo Hyun Kim, Sunwoo Hong, Younwoo Choi +3
Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment chall…
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…
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…
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
Chen-Hao Chao, Bo-Wun Cheng, Chun-Yi Lee
Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, t…