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MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Inverted bottleneck layers, which are built upon depthwise convolutions,...
04/30/2020 ∙ by Yunyang Xiong, et al. ∙
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BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
Neural architecture search (NAS) has shown promising results discovering...
03/24/2020 ∙ by Jiahui Yu, et al. ∙
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Evaluating Feature Importance Estimates
Estimating the influence of a given feature to a model prediction is cha...
06/28/2018 ∙ by Sara Hooker, et al. ∙
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Backprop Evolution
The back-propagation algorithm is the cornerstone of deep learning. Desp...
08/08/2018 ∙ by Maximilian Alber, et al. ∙
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The (Un)reliability of saliency methods
Saliency methods aim to explain the predictions of deep neural networks....
11/02/2017 ∙ by Pieter-Jan Kindermans, et al. ∙
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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it...
06/26/2017 ∙ by Kristof T. Schütt, et al. ∙
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Learning how to explain neural networks: PatternNet and PatternAttribution
DeConvNet, Guided BackProp, LRP, were invented to better understand deep...
05/16/2017 ∙ by Pieter-Jan Kindermans, et al. ∙
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Don't Decay the Learning Rate, Increase the Batch Size
It is common practice to decay the learning rate. Here we show one can u...
11/01/2017 ∙ by Samuel L. Smith, et al. ∙
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iNNvestigate neural networks!
In recent years, deep neural networks have revolutionized many applicati...
08/13/2018 ∙ by Maximilian Alber, et al. ∙
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Neural Predictor for Neural Architecture Search
Neural Architecture Search methods are effective but often use complex a...
12/02/2019 ∙ by Wei Wen, et al. ∙
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