181 citations · 1.7k across the 107 of their papers we have counts for
108 papers
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
Miaoge Li, Dongsheng Wang, Xinyang Liu +4
Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent spac…
Class-Balancing Diffusion Models
Yiming Qin, Huangjie Zheng, Jiangchao Yao +2
Diffusion-based models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies. However, such observation is only justified…
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
Tianqi Chen, Mingyuan Zhou
Learning to denoise has emerged as a prominent paradigm to design state-of-the-art deep generative models for natural images. How to use it to model the distributions of both conti…
In-Context Learning Unlocked for Diffusion Models
Zhendong Wang, Yifan Jiang, Yadong Lu +5
We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to…
POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models
Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng +2
Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big models have zero-shot ca…
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Zhendong Wang, Yifan Jiang, Huangjie Zheng +5
Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the tr…
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…
A Prototype-Oriented Clustering for Domain Shift with Source Privacy
Korawat Tanwisuth, Shujian Zhang, Pengcheng He +1
Unsupervised clustering under domain shift (UCDS) studies how to transfer the knowledge from abundant unlabeled data from multiple source domains to learn the representation of the…
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…
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
Dandan Guo, Long Tian, He Zhao +2
Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distributi…
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…
Ordinal Graph Gamma Belief Network for Social Recommender Systems
Dongsheng Wang, Chaojie Wang, Bo Chen +1
To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships betwee…
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Dandan Guo, Zhuo Li, Meixi Zheng +3
Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training s…
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou
Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often pe…
Probabilistic Conformal Prediction Using Conditional Random Samples
Zhendong Wang, Ruijiang Gao, Mingzhang Yin +2
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP…
CARD: Classification and Regression Diffusion Models
Xizewen Han, Huangjie Zheng, Mingyuan Zhou
Learning the distribution of a continuous or categorical response variable given its covariates is a fundamental problem in statistics and machine l…
Diffusion-GAN: Training GANs with Diffusion
Zhendong Wang, Huangjie Zheng, Pengcheng He +2
Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective…
ALLSH: Active Learning Guided by Local Sensitivity and Hardness
Shujian Zhang, Chengyue Gong, Xingchao Liu +3
Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples w…
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Dongsheng Wang, Dandan Guo, He Zhao +4
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is…
Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs
Huangjie Zheng, Pengcheng He, Weizhu Chen +1
Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks with a simple architecture and relatively small computational cost. The…
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning
Shentao Yang, Zhendong Wang, Huangjie Zheng +2
Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy tra…
Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
Huangjie Zheng, Pengcheng He, Weizhu Chen +1
Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffu…
A Variational Edge Partition Model for Supervised Graph Representation Learning
Yilin He, Chaojie Wang, Hao Zhang +2
Graph neural networks (GNNs), which propagate the node features through the edges and learn how to transform the aggregated features under label supervision, have achieved great su…
Bayesian Graph Contrastive Learning
Arman Hasanzadeh, Mohammadreza Armandpour, Ehsan Hajiramezanali +3
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods…