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1,761 claims from 1,082 papers are on the record. 46 have been checked so far; the other 1,715 have no check with a result yet.

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Status: Unchecked ×Keyword: LASSO ×Clear all

4 claims from 3 papers

1. Computer Science › Advanced Neural Network Applications

   Channel Pruning for Accelerating Very Deep Neural Networks

   He, Zhang and Sun · arXiv (Cornell University) · 2017

   The paper introduces a channel pruning method that speeds up trained deep convolutional networks, and reports results on VGG-16, ResNet and Xception with small losses in accuracy.

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   1. UncheckedThe authors report that their pruned VGG-16 network runs five times faster with only a 0.3% increase in error, which they describe as state-of-the-art.“Our pruned VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error.”

2. Materials Science › Machine Learning in Materials Science

   Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

   Lee, Seko, Shitara, Nakayama and Tanaka · Physical review. B./Physical review. B · 2016

   Machine learning models were built to predict G0W0 band gaps of 156 binary compounds from Kohn-Sham band gaps plus element and crystal information; the best reached an error of 0.18 eV.

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   1. UncheckedUsing only a PBE or mBJ Kohn-Sham band gap, a simple linear model predicts G0W0 band gaps of random test compounds with an error of 0.54 eV.“When the Kohn-Sham band-gap by GGA (PBE) or modified Becke-Johnson (mBJ) is used as a single predictor, OLSR model predicts the G0W0 band-gap of a randomly selected test data with the root mean square error (RMSE) of 0.54 eV.”

3. Computer Science › Advanced Neural Network Applications

   Automatic Network Pruning via Hilbert-Schmidt Independence Criterion Lasso under Information Bottleneck Principle

   Guo, Zhang, Zheng et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 2023

   The paper proposes an automatic neural network pruning method based on Information Bottleneck theory and a Hilbert-Schmidt Independence Criterion Lasso, and reports strong results on several benchmarks.

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   1. UncheckedPruning ResNet-50 with the authors' method cuts FLOPs by 56% and parameters by 50%, with a 0.08% top-1 accuracy loss on ImageNet.“With ResNet-50, we achieve a 56%-FLOPs reduction by removing 50% of the parameters, with a small loss of 0.08% in the top-1 accuracy on ImageNet.”
   2. UncheckedWith VGG-16 on CIFAR-10, the pruning method cuts computation by 60% and removes 76% of parameters while top-1 accuracy rises by 0.40%.“For example, with VGG-16, we achieve a 60%-FLOPs reduction by removing 76% of the parameters, with an improvement of 0.40% in top-1 accuracy on CIFAR-10.”