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SysML: The New Frontier of Machine Learning Systems | | Alexander Ratner
; Dan Alistarh
; Gustavo Alonso
; Peter Bailis
; Sarah Bird
; Nicholas Carlini
; Bryan Catanzaro
; Eric Chung
; Bill Dally
; Jeff Dean
; Inderjit S. Dhillon
; Alexandros Dimakis
; Pradeep Dubey
; Charles Elkan
; Grigori Fursin
; Gregory R. Ganger
; Lise Getoor
; Phillip B. Gibbons
; Garth A. Gibson
; Joseph E. Gonzalez
; Justin Gottschlich
; Song Han
; Kim Hazelwood
; Furong Huang
; Martin Jaggi
; Kevin Jamieson
; Michael I. Jordan
; Gauri Joshi
; Rania Khalaf
; Jason Knight
; Jakub Konečný
; Tim Kraska
; Arun Kumar
; Anastasios Kyrillidis
; Jing Li
; Samuel Madden
; H. Brendan McMahan
; Erik Meijer
; Ioannis Mitliagkas
; Rajat Monga
; Derek Murray
; Dimitris Papailiopoulos
; Gennady Pekhimenko
; Theodoros Rekatsinas
; Afshin Rostamizadeh
; Christopher Ré
; Christopher De Sa
; Hanie Sedghi
; Siddhartha Sen
; Virginia Smith
; Alex Smola
; Dawn Song
; Evan Sparks
; Ion Stoica
; Vivienne Sze
; Madeleine Udell
; Joaquin Vanschoren
; Shivaram Venkataraman
; Rashmi Vinayak
; Markus Weimer
; Andrew Gordon Wilson
; Eric Xing
; Matei Zaharia
; Ce Zhang
; Ameet Talwalkar
; | | Date: |
29 Mar 2019 | | Abstract: | Machine learning (ML) techniques are enjoying rapidly increasing adoption.
However, designing and implementing the systems that support ML models in
real-world deployments remains a significant obstacle, in large part due to the
radically different development and deployment profile of modern ML methods,
and the range of practical concerns that come with broader adoption. We propose
to foster a new systems machine learning research community at the intersection
of the traditional systems and ML communities, focused on topics such as
hardware systems for ML, software systems for ML, and ML optimized for metrics
beyond predictive accuracy. To do this, we describe a new conference, SysML,
that explicitly targets research at the intersection of systems and machine
learning with a program committee split evenly between experts in systems and
ML, and an explicit focus on topics at the intersection of the two. | | Source: | arXiv, 1904.3257 | | Services: | Forum | Review | PDF | Favorites | |
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