89 citations · 191 across the 23 of their papers we have counts for
7 papers · 1 filter
Dynamic Sparse Network for Time Series Classification: Learning What to "see''
Qiao Xiao, Boqian Wu, Yu Zhang +4
The receptive field (RF), which determines the region of time series to be ``seen'' and used, is critical to improve the performance for time series classification (TSC). However,…
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…
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Shiwei Liu, Tianlong Chen, Xiaohan Chen +7
Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to…
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…
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance
Shiwei Liu, Yuesong Tian, Tianlong Chen +1
Generative adversarial networks (GANs) have received an upsurging interest since being proposed due to the high quality of the generated data. While achieving increasingly impressi…
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Shiwei Liu, Tianlong Chen, Xiaohan Chen +4
Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this p…