Pieter-Jan Kindermans

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Google ∙ Berlin Institute of Technology (Technische Universität Berlin)

Discovering Multi-Hardware Mobile Models via Architecture Search

Developing efficient models for mobile phones or other on-device deploym...
08/18/2020 ∙ by Grace Chu, et al. ∙ 0 ∙ share

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Can weight sharing outperform random architecture search? An investigation with TuNAS

Efficient Neural Architecture Search methods based on weight sharing hav...
08/13/2020 ∙ by Gabriel Bender, et al. ∙ 15 ∙ share

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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. ∙ 15 ∙ share

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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. ∙ 6 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 4 ∙ share

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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. ∙ 4 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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Pieter-Jan Kindermans

Featured Co-authors

Quoc V. Le

Klaus-Robert Müller

Wojciech Samek

Yiran Chen

Bo Chen

Hai Li

Thomas Huang

Vikas Singh

Ruoming Pang

Been Kim

Dumitru Erhan

Discovering Multi-Hardware Mobile Models via Architecture Search

Can weight sharing outperform random architecture search? An investigation with TuNAS

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

Neural Predictor for Neural Architecture Search

iNNvestigate neural networks!

Backprop Evolution

Evaluating Feature Importance Estimates

The (Un)reliability of saliency methods

Don't Decay the Learning Rate, Increase the Batch Size

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Learning how to explain neural networks: PatternNet and PatternAttribution