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Automatically Tracking Metadata and Provenance of Machine Learning Experiments | Sebastian Schelter
Automatically Tracking Metadata and Provenance of Machine Learning Experiments Sebastian Schelter, Joos-Hendrik Böse, Johannes Kirschnick, Thoralf Klein, Stephan Seufert Abstract We present a lightweight system to extract, store and manage metadata and provenance information of common artifacts in machine learning (ML) experiments: datasets, models, predictions, evaluations and training runs. Our
Systems for ML
Workshop on Systems for ML and Open Source Software at NeurIPS 2018 Workshop on Systems for ML A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. The goal of this
ML Systems
Workshop on ML Systems at NIPS 2017 December 8, 2017 Home Call for Papers Schedule Speakers Accepted Papers ML Systems Workshop A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale le
NIPS2017チュートリアルによるDeep Learningの現状まとめ - 渋谷駅前で働くデータサイエンティストのブログ
Deep Learning_ Practice and Trends - final.pdf - Google ドライブ 明けましておめでとうございます、本年もよろしくお願いいたします。新年一発目の記事はただの備忘録です。 こちらは、旧知のバクフーCEO柏野さん(@yutakashino)からご紹介いただいたNIPS2017チュートリアル。 （´-`）.｡oO( 今年は深層学習にいい加減なことを言う人が近くにいた，らせめてNIPS2017のこのチュートリアルくらいは押さえてよ，言うことにします．"Deep Learning: Practice and Trends Tutorial " https://t.co/Q23KftmdXp https://t.co/tK7mnwmgtJ たぶんこの辺りが「常識」でしょうか… )— Yuta Kashino (@yutakashino) 2018年
Summary of NIPS 2016
The 30th annual Neural Information Processing Systems (NIPS) conference took place in Barcelona in early December. In this post, I share my thoughts on the conference. Let me know your thoughts in the comments section! Basic outline: State of AI Research and trends in 2016 Talks and papers that I liked Predictions for 2017 Miscellaneous notes Academic, industry, and public interest in Artificial I
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NIPS 2018
http://learningsys.org/nips17/assets/papers/paper_32.pdf
http://learningsys.org/nips17/assets/papers/paper_22.pdf
http://learningsys.org/nips17/assets/papers/paper_19.pdf
http://learningsys.org/nips17/assets/papers/paper_14.pdf
http://learningsys.org/nips17/assets/papers/paper_11.pdf
http://learningsys.org/nips17/assets/papers/paper_1.pdf
http://learningsys.org/nips17/assets/papers/paper_13.pdf
What’s your ML test score? A rubric for ML production systems
[PDF]Machine Learning for Systems and Systems for Machine Learning Jeff Dean Google Brain team
Understanding Dropout