19 citations · 41 across the 10 of their papers we have counts for
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cs.LG2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund +20
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the…
cs.LG2020
Pruning Convolutional Filters using Batch Bridgeout
Najeeb Khan, Ian Stavness
State-of-the-art computer vision models are rapidly increasing in capacity, where the number of parameters far exceeds the number required to fit the training set. This results in…
cs.LG2019★ 7 cited
Sparseout: Controlling Sparsity in Deep Networks
Najeeb Khan, Ian Stavness
Dropout is commonly used to help reduce overfitting in deep neural networks. Sparsity is a potentially important property of neural networks, but is not explicitly controlled by Dr…