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Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training


Feb 17, 2021
Kai Sheng Tai, Peter Bailis, Gregory Valiant


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Leveraging Organizational Resources to Adapt Models to New Data Modalities


Aug 23, 2020
Sahaana Suri, Raghuveer Chanda, Neslihan Bulut, Pradyumna Narayana, Yemao Zeng, Peter Bailis, Sugato Basu, Girija Narlikar, Christopher Re, Abishek Sethi

* PVLDB,13(12): 3396-3410, 2020 

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Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics


Jul 25, 2020
Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia


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Similarity Search for Efficient Active Learning and Search of Rare Concepts


Jun 30, 2020
Cody Coleman, Edward Chou, Sean Culatana, Peter Bailis, Alexander C. Berg, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz


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Chromatic Learning for Sparse Datasets


Jun 06, 2020
Vladimir Feinberg, Peter Bailis

* 15 pages, 8 figures, under review 

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Model Assertions for Monitoring and Improving ML Models


Mar 11, 2020
Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia

* MLSys 2020 

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MLPerf Training Benchmark


Oct 30, 2019
Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia


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Selection Via Proxy: Efficient Data Selection For Deep Learning


Jun 26, 2019
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia


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Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference


Jun 03, 2019
Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia


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CrossTrainer: Practical Domain Adaptation with Loss Reweighting


May 07, 2019
Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis


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SysML: The New Frontier of Machine Learning Systems


May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, 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, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, 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


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Equivariant Transformer Networks


Jan 25, 2019
Kai Sheng Tai, Peter Bailis, Gregory Valiant


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LIT: Block-wise Intermediate Representation Training for Model Compression


Oct 02, 2018
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia


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Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark


Jun 04, 2018
Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Re, Matei Zaharia


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Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data


May 30, 2018
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant

* 17 pages 

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Sketching Linear Classifiers over Data Streams


Apr 06, 2018
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant

* Full version of paper appearing at SIGMOD 2018 with more detailed proofs of theoretical results. Code available at https://github.com/stanford-futuredata/wmsketch 

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NoScope: Optimizing Neural Network Queries over Video at Scale


Aug 08, 2017
Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

* PVLDB 2017 

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Infrastructure for Usable Machine Learning: The Stanford DAWN Project


Jun 09, 2017
Peter Bailis, Kunle Olukotun, Christopher Re, Matei Zaharia


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