18 citations · 66 across the 17 of their papers we have counts for
11 papers · 1 filter
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation
Ricky Maulana Fajri, Yulong Pei, Lu Yin +1
Despite significant advancements in active learning and adversarial attacks, the intersection of these two fields remains underexplored, particularly in developing robust active le…
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…
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…
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…
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Tianjin Huang, Tianlong Chen, Meng Fang +8
Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully…
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Lu Yin, Shiwei Liu, Meng Fang +3
Lottery tickets (LTs) is able to discover accurate and sparse subnetworks that could be trained in isolation to match the performance of dense networks. Ensemble, in parallel, is o…
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Lu Yin, Vlado Menkovski, Meng Fang +5
Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically…
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Shiwei Liu, Tianlong Chen, Xiaohan Chen +7
Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised a lot of attention currently on post-training pruning (iterative magnitude pruning), and…
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
Shiwei Liu, Lu Yin, Decebal Constantin Mocanu +1
In this paper, we introduce a new perspective on training deep neural networks capable of state-of-the-art performance without the need for the expensive over-parameterization by p…
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
Lu Yin, Vlado Menkovski, Mykola Pechenizkiy
Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the…