11 citations · 35 across the 12 of their papers we have counts for
12 papers
BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
Zhibin Duan, Yuhong Wang, Jiahong Fu +3
While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational f…
Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling
Zhibin Duan, Guowei Rong, Zhuo Li +3
Reward models learned from human preferences are central to aligning large language models (LLMs) via reinforcement learning from human feedback, yet they are often vulnerable to r…
Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision Layer
Xinyue Hu, Zhibin Duan, Bo Chen +1
Although deep neural networks have demonstrated significant success due to their powerful expressiveness, most models struggle to meet practical requirements for uncertainty estima…
Treating Brain-inspired Memories as Priors for Diffusion Model to Forecast Multivariate Time Series
Muyao Wang, Wenchao Chen, Zhibin Duan +1
Forecasting Multivariate Time Series (MTS) involves significant challenges in various application domains. One immediate challenge is modeling temporal patterns with the finite len…
A Non-negative VAE:the Generalized Gamma Belief Network
Zhibin Duan, Tiansheng Wen, Muyao Wang +2
The gamma belief network (GBN), often regarded as a deep topic model, has demonstrated its potential for uncovering multi-layer interpretable latent representations in text data. I…
Disentangled Generative Graph Representation Learning
Xinyue Hu, Zhibin Duan, Xinyang Liu +6
Recently, generative graph models have shown promising results in learning graph representations through self-supervised methods. However, most existing generative graph representa…
Beyond Spectral Decomposition: Bayesian Contrastive Learning and its Non-negative Formulation via Factor Analysis
Zhibin Duan, Tiansheng Wen, Yifei Wang +3
Factor analysis, often regarded as a Bayesian variant of matrix factorization, offers superior capabilities in capturing uncertainty, modeling complex dependencies, and ensuring ro…
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
Xinyang Liu, Dongsheng Wang, Bowei Fang +5
For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, whic…
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
Yishi Xu, Dongsheng Wang, Bo Chen +3
Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, l…
Knowledge-Aware Bayesian Deep Topic Model
Dongsheng Wang, Yishi Xu, Miaoge Li +4
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Although embedded topic models (ETMs) and its variants have gained…
TopicNet: Semantic Graph-Guided Topic Discovery
Zhibin Duan, Yishi Xu, Bo Chen +3
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic…
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Zhibin Duan, Dongsheng Wang, Bo Chen +5
Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic…