48 citations · 57 across the 7 of their papers we have counts for
7 papers
How Far Can Sharpness and Complexity Jointly Explain Generalization?
Ziyu Cheng, Xitong Zhang, Longxiu Huang +1
Large-scale empirical studies have revealed that many widely used generalization measures correlate only weakly with generalization, suggesting that relying on a single factor is i…
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization
Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun +3
We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sa…
Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition
Xitong Zhang, Ismail R. Alkhouri, Rongrong Wang
Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNN…
Harnessing Trust in Directed Graphs: Redefining Robustness of Graph Learning
Zhichao Hou, Xitong Zhang, Wei Wang +2
Existing research on robust Graph Neural Networks (GNNs) focuses predominantly on undirected graphs, overlooking the trustworthiness inherent in the link directions of directed gra…
Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent
Avrajit Ghosh, He Lyu, Xitong Zhang +1
It is well known that the finite step-size () in Gradient Descent (GD) implicitly regularizes solutions to flatter minima. A natural question to ask is "Does the momentum parame…
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
Yixuan He, Xitong Zhang, Junjie Huang +3
Networks are ubiquitous in many real-world applications (e.g., social networks encoding trust/distrust relationships, correlation networks arising from time series data). While man…
MagNet: A Neural Network for Directed Graphs
Xitong Zhang, Yixuan He, Nathan Brugnone +2
The prevalence of graph-based data has spurred the rapid development of graph neural networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets natural…