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MLSys: Workshop on Systems for ML
Aparna Lakshmiratan · Siddhartha Sen · Joseph Gonzalez · Dan Crankshaw · Sarah Bird

Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ East Meeting Rooms 11 + 12
Event URL: http://learningsys.org/neurips19/ »

A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This has been accelerated 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 large-scale ML systems. In particular, we aim to elicit new connections among these diverse fields, identifying theory, tools and design principles tailored to practical machine learning workflows. We also want to think about best practices for research in this area and how to evaluate it. The workshop will cover state of the art ML and AI platforms and algorithm toolkits (e.g. TensorFlow, PyTorch1.0, MXNet etc.), as well as dive into machine learning-focused developments in distributed learning platforms, programming languages, data structures, hardware accelerators, benchmarking systems and other topics.

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

Our plan is to run this workshop annually co-located with one ML venue and one Systems venue, to help build a strong community which we think will complement newer conferences like SysML targeting research at the intersection of systems and machine learning. We believe this dual approach will help to create a low barrier to participation for both communities.

This workshop is part two of a two-part series with one day focusing on ML for Systems and the other on Systems for ML. Although the two workshops are being led by different organizers, we are coordinating our call for papers to ensure that the workshops complement each other and that submitted papers are routed to the appropriate venue.

Fri 8:30 a.m. - 8:40 a.m.
Welcome (Talk)
Fri 8:40 a.m. - 9:10 a.m.
Keynote 1: Machine Learning Reproducibility: An update from the NeurIPS 2019 Reproducibility Co-Chairs, Joelle Pineau, McGill University and Facebook (Talk)
Fri 9:10 a.m. - 9:30 a.m.
Contributed Talk: SLIDE : Training Deep Neural Networks with Large Outputs on a CPU faster than a V100-GPU (Talk)
Fri 9:30 a.m. - 9:50 a.m.
Contributed Talk: NeMo: A Toolkit for Building AI Applications Using Neural Modules (Talk)
Fri 9:50 a.m. - 10:00 a.m.
Poster Overview (Talk)
Fri 10:00 a.m. - 11:10 a.m.
Posters and Coffee (Poster Session)
Sameer Kumar, Tomasz Kornuta, Oleg Bakhteev, Hui Guan, Xiaomeng Dong, Minsik Cho, Soeren Laue, Theodore Vasiloudis, Andreea Anghel, Erik Wijmans, Zeyuan Shang, Oleksii Kuchaiev, Ji Lin, Susan Zhang, Ligeng Zhu, Beidi Chen, VINU Joseph, Jialin Ding, Jonathan Raiman, Ahnjae Shin, Vithu Thangarasa, Anush Sankaran, Akhil Mathur, Martino Dazzi, Markus Löning, Darryl Ho, Emanuel Zgraggen, Supun Nakandala, Tomasz Kornuta, Rita Kuznetsova
Fri 11:10 a.m. - 11:40 a.m.
Keynote 2: Vivienne Sze, MIT (Talks)
Fri 11:40 a.m. - 12:00 p.m.
Contributed Talk: 5 Parallel Prism: A Topology for Pipelined Implementations of Convolutional Neural Networks Using Computational Memory (Talk)
Fri 12:00 p.m. - 1:30 p.m.
Lunch (Break)
Fri 1:30 p.m. - 3:30 p.m.
Systems Bonanza (10 minutes each) PyTorch TensorFlow Keras TVM Ray ONNX Runtime CoreML Flux MLFlow MLPerf Microsoft RL Systems MXNet (Talk)
Fri 3:30 p.m. - 4:30 p.m.
Break and Poster Session (Poster Session)
Fri 3:30 p.m. - 4:30 p.m.
Posters and Coffee (Poster Session)
Fri 4:30 p.m. - 5:00 p.m.
Keynote 3 (Talk)
Fri 5:00 p.m. - 5:20 p.m.
Contributed Talk: LISA: Towards Learned DNA Sequence Search (Discussion Panel)
Fri 5:20 p.m. - 5:30 p.m.
Closing (Talk)

Author Information

Aparna Lakshmiratan (Facebook)
Siddhartha Sen (Microsoft Research)
Joseph Gonzalez (UC Berkeley)
Dan Crankshaw (UC Berkeley)
Sarah Bird (Microsoft)

Sarah’s work focuses on research and emerging technology strategy for AI products in Azure. Sarah works to accelerate the adoption and positive impact of AI by bringing together the latest innovations in research with the best of open source and product expertise to create new tools and technologies. Sarah is currently leading Responsible AI for the Azure Cognitive Services. Prior to joining the Cognitive Services, Sarah lead the development of responsible AI tools in Azure Machine Learning. She is an active member of the Microsoft AETHER committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Sarah was one of the founding researchers in the Microsoft FATE research group and prior to joining Microsoft worked on AI fairness in Facebook. Sarah is active contributor to the open source ecosystem, she co-founded ONNX, Fairlearn, and OpenDP’s SmartNoise was a leader in the Pytorch 1.0 and InterpretML projects. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She co-founded the MLSys research conference and the Learning Systems workshops. She has a Ph.D. in computer science from UC Berkeley advised by Dave Patterson, Krste Asanovic, and Burton Smith.

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