MLconf San Francisco 2017 Speaker Resources
Franziska Bell, Data Science Manager on the Platform Team, Uber
Gal and Ghahramani (2016), A Theoretically Grounded Application of Dropout in Recurrent Neural Networks, NIPS
Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks (Laptev, Smyl, Shanmugam): https://eng.uber.com/neural-
Josh Wills, Head of Data Engineering, Slack
Jonas Schneider, Head of Engineering for Robotics, OpenAI
Robots that Learn: https://blog.openai.com/
- Dota 2: https://blog.openai.com/dota-
2/ & https://blog.openai.com/more- on-dota-2/
- Kubernetes at OpenAI: https://blog.openai.com/
infrastructure-for-deep- learning/ & https://www.youtube.com/watch? v=v4N3Krzb8Eg
- Trust Region Policy Optimization (TRPO): https://arxiv.org/abs/1502.
- Proximal Policy Optimization (PPO): https://blog.openai.com/
- Evolution Strategies: https://blog.openai.com/
- Deep Deterministic Policy Gradients (DDPG): https://arxiv.org/abs/1509.
- Hindsight Experience Replay (HER): https://arxiv.org/abs/1707.
- Jenkins Continuous Integration: https://jenkins.io/
LN Renganarayana, Architect, ML Platform and Services, Workday
Madhura Dudhgaonkar, Head of Engineering, Search, Data Science and Machine Learning, Workday
Michael Alcorn, Sr. Software Engineer, Red Hat Inc.
- Karl Weiss, Taghi M. Khoshgoftaar, DingDing Wang, A survey of transfer learning
- Goodfellow, et al; Deep Learning, Ch. 15: Representation Learning
- Bengio et al, Representation Learning: A Review and New Perspectives
- NVIDIA Blog: Introduction to Neural Machine Translation with GPUs (Part 2)
- Mikolov et al. (2013) Efficient Estimation of Word Representations inVector Space
Deeplearning4j – “Word2vec”
- Blog Post: Janelle Shane: New paint colors invented by neural network
- Le and Mikolov (2014) Distributed Representations of Sentences and Documents
- “NLP 05: From Word2vec to Doc2vec: a simple example with Gensim”
- Wang and Zemel (2016) Classifying NBA Offensive Plays Using Neural Networks
- Github: airalcorn2/ Deep-Semantic-
- Github: airalcorn2/ batter-pitcher-2vec
- Github Blog: “Learning to Coach Football“
Tamara G. Kolda, Distinguished Member of Technical Staff, Sandia National Labs
Xavier Amatriain, Co-Founder and CTO, Curai
- “Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base” . Shwe et al. 1991.
- “Computer-assisted diagnostic decision support: history, challenges, and possible paths forward” Miller. 2009.
- “Mining Biomedical Ontologies and Data Using RDF Hypergraphs” Liu et al. 2013.
- “Health Recommender Systems: Concepts, Requirements, Technical Basics & Challenges”, Wiesner & Pfeifer, 2014.
- “A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care” Longhurst et al. 2014.
- “Building the graph of medicine from millions of clinical narratives” Finlayson et al. 2014.
- “Comparison of Physician and Computer Diagnostic Accuracy” Semigran et al. 2016.
- “Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization”. Joshi et al. 2016.
- “Clinical Tagging with Joint Probabilistic Models” . Halpern et al. 2016.
- “Deep Patient: An Unsupervised Representation to Predict the Future of Patients from EHR”. Miotto et al. 2016.
- “Learning a Health Knowledge Graph from Electronic Medical Records” Rotmensch et al. 2017.
- “Clustering Patients with Tensor Decomposition”. Ruffini et al. 2017.
- “Patient Similarity Using Population Statistics and Multiple Kernel Learning”. Conroy et al. 2017.
- “Diagnostic Inferencing via Clinical Concept Extraction with Deep Reinforcement Learning”. Ling et al. 2017.
- “Generating Multi-label Discrete Patient Records using Generative Adversarial Networks” Choi et al. 2017
- Suresh, H., Szolovits, P., & Ghassemi, M. (2017, March 20). The Use of Autoencoders for Discovering Patient Phenotypes. arXiv.org.