83 citations · 286 across the 16 of their papers we have counts for
9 papers · 1 filter
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +4
Modern deep learning models are over-parameterized, where the optimization setup strongly affects the generalization performance. A key element of reliable optimization for these s…
Geometric Graph Representation Learning via Maximizing Rate Reduction
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +3
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation…
Towards Interaction Detection Using Topological Analysis on Neural Networks
Zirui Liu, Qingquan Song, Kaixiong Zhou +3
Detecting statistical interactions between input features is a crucial and challenging task. Recent advances demonstrate that it is possible to extract learned interactions from tr…
Sub-Architecture Ensemble Pruning in Neural Architecture Search
Yijun Bian, Qingquan Song, Mengnan Du +3
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.…
Auto-GNN: Neural Architecture Search of Graph Neural Networks
Kaixiong Zhou, Qingquan Song, Xiao Huang +1
Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials a…
Techniques for Automated Machine Learning
Yi-Wei Chen, Qingquan Song, Xia Hu
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists f…
Coupled Variational Recurrent Collaborative Filtering
Qingquan Song, Shiyu Chang, Xia Hu
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Alt…
Multi-Label Adversarial Perturbations
Qingquan Song, Haifeng Jin, Xiao Huang +1
Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating…
Auto-Keras: An Efficient Neural Architecture Search System
Haifeng Jin, Qingquan Song, Xia Hu
Neural architecture search (NAS) has been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually suffer from expensive com…