24 citations · 55 across the 11 of their papers we have counts for
12 papers
Your Classifier Can Do More: Towards Balancing the Gaps in Classification, Robustness, and Generation
Kaichao Jiang, He Wang, Xiaoshuai Hao +5
Joint Energy-based Models (JEMs) are well known for their ability to unify classification and generation within a single framework. Despite their promising generative and discrimin…
TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery
Kun Wu, Zixu Wang, Xiulong Yang +4
As the primary mRNA delivery vehicles, ionizable lipid nanoparticles (LNPs) exhibit excellent safety, high transfection efficiency, and strong immune response induction. However, t…
M-EBM: Towards Understanding the Manifolds of Energy-Based Models
Xiulong Yang, Shihao Ji
Energy-based models (EBMs) exhibit a variety of desirable properties in predictive tasks, such as generality, simplicity and compositionality. However, training EBMs on high-dimens…
APSNet: Attention Based Point Cloud Sampling
Yang Ye, Xiulong Yang, Shihao Ji
Processing large point clouds is a challenging task. Therefore, the data is often downsampled to a smaller size such that it can be stored, transmitted and processed more efficient…
Towards Bridging the Performance Gaps of Joint Energy-based Models
Xiulong Yang, Qing Su, Shihao Ji
Can we train a hybrid discriminative-generative model within a single network? This question has recently been answered in the affirmative, introducing the field of Joint Energy-ba…
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model
Xiulong Yang, Sheng-Min Shih, Yinlin Fu +2
Diffusion Denoising Probability Models (DDPM) and Vision Transformer (ViT) have demonstrated significant progress in generative tasks and discriminative tasks, respectively, and th…
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction
Mingchen Li, Junfan Chen, Samuel Mensah +3
Due to the incompleteness of knowledge graphs (KGs), zero-shot link prediction (ZSLP) which aims to predict unobserved relations in KGs has attracted recent interest from researche…
JEM++: Improved Techniques for Training JEM
Xiulong Yang, Shihao Ji
Joint Energy-based Model (JEM) is a recently proposed hybrid model that retains strong discriminative power of modern CNN classifiers, while generating samples rivaling the quality…
Improving Text-to-Image Synthesis Using Contrastive Learning
Hui Ye, Xiulong Yang, Martin Takac +2
The goal of text-to-image synthesis is to generate a visually realistic image that matches a given text description. In practice, the captions annotated by humans for the same imag…
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
Xiulong Yang, Hui Ye, Yang Ye +2
Joint Energy-based Model (JEM) of Grathwohl et al. shows that a standard softmax classifier can be reinterpreted as an energy-based model (EBM) for the joint distribution p(x,y); t…
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention
Krishanu Sarker, Xiulong Yang, Yang Li +2
The success of Deep Neural Networks (DNNs) highly depends on data quality. Moreover, predictive uncertainty makes high performing DNNs risky for real-world deployment. In this pape…
Learning with Multiplicative Perturbations
Xiulong Yang, Shihao Ji
Adversarial Training (AT) and Virtual Adversarial Training (VAT) are the regularization techniques that train Deep Neural Networks (DNNs) with adversarial examples generated by add…