33 citations · 178 across the 28 of their papers we have counts for
10 papers · 1 filter
FLSL: Feature-level Self-supervised Learning
Qing Su, Anton Netchaev, Hai Li +1
Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg,MOCOv3) target primarily on representations at instance level and do not generalize well to dense predict…
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…
Dep-: Improving -based Network Sparsification via Dependency Modeling
Yang Li, Shihao Ji
Training deep neural networks with an regularization is one of the prominent approaches for network pruning or sparsification. The method prunes the network during training b…
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…
Adversarial Privacy Preserving Graph Embedding against Inference Attack
Kaiyang Li, Guangchun Luo, Yang Ye +3
Recently, the surge in popularity of Internet of Things (IoT), mobile devices, social media, etc. has opened up a large source for graph data. Graph embedding has been proved extre…
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…
Sparse Graph Attention Networks
Yang Ye, Shihao Ji
Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many prac…
-ARM: Network Sparsification via Stochastic Binary Optimization
Yang Li, Shihao Ji
We consider network sparsification as an -norm regularized binary optimization problem, where each unit of a neural network (e.g., weight, neuron, or channel, etc.) is attache…
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
Shihao Ji, S. V. N. Vishwanathan, Nadathur Satish +2
We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated…