18 citations · 66 across the 17 of their papers we have counts for
7 papers · 1 filter
REST: Enhancing Group Robustness in DNNs through Reweighted Sparse Training
Jiaxu Zhao, Lu Yin, Shiwei Liu +2
The deep neural network (DNN) has been proven effective in various domains. However, they often struggle to perform well on certain minority groups during inference, despite showin…
A Structural-Clustering Based Active Learning for Graph Neural Networks
Ricky Maulana Fajri, Yulong Pei, Lu Yin +1
In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown effectiveness. However, a common challenge in these applications is the underutilization of cr…
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
Boqian Wu, Qiao Xiao, Shiwei Liu +5
Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computa…
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Tianjin Huang, Shiwei Liu, Tianlong Chen +6
Despite the fact that adversarial training has become the de facto method for improving the robustness of deep neural networks, it is well-known that vanilla adversarial training s…
Are Large Kernels Better Teachers than Transformers for ConvNets?
Tianjin Huang, Lu Yin, Zhenyu Zhang +5
This paper reveals a new appeal of the recently emerged large-kernel Convolutional Neural Networks (ConvNets): as the teacher in Knowledge Distillation (KD) for small-kernel ConvNe…
Dynamic Sparsity Is Channel-Level Sparsity Learner
Lu Yin, Gen Li, Meng Fang +7
Sparse training has received an upsurging interest in machine learning due to its tantalizing saving potential for the entire training process as well as inference. Dynamic sparse…
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Zahra Atashgahi, Xuhao Zhang, Neil Kichler +5
Feature selection that selects an informative subset of variables from data not only enhances the model interpretability and performance but also alleviates the resource demands. R…