Bitte vorab das Paper lesen! Track: Graph Networks and Bioinformatics Thema: Neural Message Passing for Quantum Chemistry Paper: https://arxiv.org/abs/1704.01212 Oral: https://vimeo.com/238221016 Github: https://github.com/priba/nmp_qc Wir sind im Informa... Read More | Share it now!

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Bitte vorab das Paper lesen! Track: – Thema: Relational inductive biases, deep learning, and graph networks Paper: https://arxiv.org/abs/1806.01261 Wir sind im Informatik-Gebäude des KIT (50.34), Raum -120. Abstract Artificial intelligence (AI) has ... Read More | Share it now!

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Hey all! This week we are going to discuss „Deep Image Prior“ by Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Links: * Paper https://arxiv.org/abs/1711.10925 * Blog https://dmitryulyanov.github.io/deep_image_prior * Code https://githu... Read More | Share it now!

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111. PDG: Learning and Using the Arrow of Time

Hi all: Let’s discuss the CVPR paper „Learning and Using the Arrow of Time“ (http://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Learning_and_Using_CVPR_2018_paper.pdf) ... Read More | Share it now!

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109. PDG: Neural Discrete Representation Learning

Bitte vorab das Paper lesen! Track: – Thema: Neural Discrete Representation Learning Paper: https://arxiv.org/abs/1711.00937 Wir sind im Informatik-Gebäude des KIT (50.34), Raum -120. Meetup: https://www.meetup.com/de-DE/karlsruhe-ai/events/25246010... Read More | Share it now!

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108. PDG: CausalGAN

Bitte vorab das Paper lesen! Track: Interpretable and Explainable ML Thema: CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training Paper: https://arxiv.org/abs/1709.02023 Wir sind im Informatik-Gebäude des KIT (50.34), Raum -120. ... Read More | Share it now!

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107. PDG:  Understanding Black-box Predictions

Bitte vorab das Paper lesen! Track: Interpretable and Explainable ML Thema: Understanding Black-box Predictions via Influence Functions Paper: https://arxiv.org/abs/1703.04730 Wir sind im Informatik-Gebäude des KIT (50.34), Raum -120. Meetup: https://www.... Read More | Share it now!

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106. PDG:  InfoGAN: Interpretable Representation Learning

Bitte vorab das Paper lesen! Track: Interpretable and Explainable ML Thema: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Paper: https://arxiv.org/abs/1606.03657 Wir sind im Informatik-Gebäude des KIT... Read More | Share it now!

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Robotics Hackathon

Scientists and Students Track “Hackathon” Task/Application We will provide a working environment as well as the required hard- and software to enable you to work on robotics topics during the hackathon. Furthermore we will give you advice, guidance and su... Read More | Share it now!

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105. PDG: Explaining the Predictions of Any Classifier

Bitte vorab das Paper lesen! Track: Interpretable and Explainable ML Thema: „Why Should I Trust You?“: Explaining the Predictions of Any Classifier Paper: https://arxiv.org/abs/1602.04938 Wir sind im Informatik-Gebäude des KIT (50.34), Raum -1... Read More | Share it now!

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