Program Highlights »
Fri Dec 8th 08:00 AM -- 06:30 PM @ S1
ML Systems Workshop @ NIPS 2017
Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw

Workshop Home Page

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 workshop is to bring together experts working at the crossroads of machine learning, system design and software engineering to explore the challenges faced when building practical large-scale ML systems. In particular, we aim to elicit new connections among these diverse fields, and identify tools, best practices and design principles. We also want to think about how to do research in this area and properly evaluate it. The workshop will cover ML and AI platforms and algorithm toolkits, as well as dive into machine learning-focused developments in distributed learning platforms, programming languages, data structures, GPU processing, and other topics.

This workshop will follow the successful model we have previously run at ICML, NIPS and SOSP 2017.

Our plan is to run this workshop annually at one ML venue and one Systems venue, and eventually merge these communities into a full conference venue. We believe this dual approach will help to create a low barrier to participation for both communities.

08:45 AM Opening Remarks (Talk)
09:00 AM Invited Talk: Ray: A distributed execution engine for emerging AI applications, Ion Stoica, UC Berkeley (Talk)
Ion Stoica
09:20 AM Contributed Talk 1: The Case for Learning Database Indexes (Talk)
09:40 AM Invited Talk: Federated Multi-Task Learning, Virginia Smith, Stanford University (Talk)
Virginia Smith
10:00 AM Poster Previews: 1 min lightning talks (Talks)
11:30 AM Invited Talk: Accelerating Persistent Neural Networks at Datacenter Scale, Daniel Lo, Microsoft Research (Talk)
Daniel Lo
11:50 AM Contributed Talk 2: DLVM: A modern compiler framework for neural network DSLs (Talk)
12:10 PM Lunch (Break)
01:20 PM Updates from Current ML Systems (TensorFlow, PyTorch, Caffe2, CNTK, MXNet, TVM, Clipper, MacroBase, ModelDB) (Talk)
Rajat Monga, Soumith Chintala, Cha Zhang, Tianqi Chen, Dan Crankshaw, Kai Sheng Tai, Andrew Tulloch, Manasi Vartak
02:50 PM Invited Talk: Machine Learning for Systems and Systems for Machine Learning, Jeff Dean, Google Brain (Talk)
Jeff Dean
03:20 PM Invited Talk: Creating an Open and Flexible ecosystem for AI models with ONNX, Sarah Bird, Dmytro Dzhulgakov, Facebook Research (Talk)
Sarah Bird
03:40 PM Posters and Coffee (Poster Session)
Jean-Baptiste Tristan, Yunseong Lee, Anna Veronika Dorogush, Shohei Hido, Michael Terry, Mennatullah Siam, Hidemoto Nakada, Cody Coleman, Jung-Woo Ha, Hao Zhang, Adam Stooke, Chen Meng, Chris Kappler, Lane Schwartz, Christopher Olston, Sebastian Schelter, Minmin Sun, Daniel Kang, Waldemar Hummer, Jichan Chung, Tim Kraska, Kannan Ramchandran, Nick Hynes, Christoph Boden, Donghyun Kwak
04:30 PM Contributed Talk 3: NSML: A Machine Learning Platform That Enables You to Focus on Your Models (Talk)
04:50 PM Contributed Talk 4: DAWNBench: An End-to-End Deep Learning Benchmark and Competition (Talk)
05:10 PM Panel (Discussion Panel)
Garth Gibson, Joseph Gonzalez, John Langford, Dawn Song