33 citations · 178 across the 28 of their papers we have counts for
6 papers · 1 filter
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…
Toward Filament Segmentation Using Deep Neural Networks
Azim Ahmadzadeh, Sushant S. Mahajan, Dustin J. Kempton +2
We use a well-known deep neural network framework, called Mask R-CNN, for identification of solar filaments in full-disk H-alpha images from Big Bear Solar Observatory (BBSO). The…
Neural Image Compression and Explanation
Xiang Li, Shihao Ji
Explaining the prediction of deep neural networks (DNNs) and semantic image compression are two active research areas of deep learning with a numerous of applications in decision-c…
Neural Plasticity Networks
Yang Li, Shihao Ji
Neural plasticity is an important functionality of human brain, in which number of neurons and synapses can shrink or expand in response to stimuli throughout the span of life. We…
-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…