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Project CGX: Scalable Deep Learning on Commodity GPUs


Nov 17, 2021
Ilia Markov, Hamidreza Ramezanikebrya, Dan Alistarh


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Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization


Jul 09, 2021
Elias Frantar, Eldar Kurtic, Dan Alistarh


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SSSE: Efficiently Erasing Samples from Trained Machine Learning Models


Jul 08, 2021
Alexandra Peste, Dan Alistarh, Christoph H. Lampert


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AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks


Jun 23, 2021
Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh


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NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization


May 01, 2021
Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy

* This entry is redundant and was created in error. See arXiv:1908.06077 for the latest version 

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Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks


Jan 31, 2021
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste

* 90 pages, 26 figures 

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Byzantine-Resilient Non-Convex Stochastic Gradient Descent


Dec 28, 2020
Zeyuan Allen-Zhu, Faeze Ebrahimian, Jerry Li, Dan Alistarh


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Adaptive Gradient Quantization for Data-Parallel SGD


Oct 23, 2020
Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel Roy, Ali Ramezani-Kebrya

* Accepted at the conference on Neural Information Processing Systems (NeurIPS 2020) 

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Improved Communication Lower Bounds for Distributed Optimisation


Oct 16, 2020
Dan Alistarh, Janne H. Korhonen


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Stochastic Gradient Langevin with Delayed Gradients


Jun 12, 2020
Vyacheslav Kungurtsev, Bapi Chatterjee, Dan Alistarh


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Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging


Apr 30, 2020
Shigang Li, Tal Ben-Nun, Dan Alistarh, Salvatore Di Girolamo, Nikoli Dryden, Torsten Hoefler


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WoodFisher: Efficient second-order approximations for model compression


Apr 29, 2020
Sidak Pal Singh, Dan Alistarh


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Relaxed Scheduling for Scalable Belief Propagation


Feb 25, 2020
Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen


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On the Sample Complexity of Adversarial Multi-Source PAC Learning


Feb 24, 2020
Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert


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Distributed Mean Estimation with Optimal Error Bounds


Feb 24, 2020
Dan Alistarh, Saleh Ashkboos, Peter Davies

* 19 pages, 4 figures 

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Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent


Jan 16, 2020
Dan Alistarh, Bapi Chatterjee, Vyacheslav Kungurtsev


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PopSGD: Decentralized Stochastic Gradient Descent in the Population Model


Oct 27, 2019
Giorgi Nadiradze, Amirmojtaba Sabour, Aditya Sharma, Ilia Markov, Vitaly Aksenov, Dan Alistarh


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Powerset Convolutional Neural Networks


Oct 04, 2019
Chris Wendler, Dan Alistarh, Markus Püschel

* to appear in Proc. Neural Information Processing Systems (NeurIPS), 2019 

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Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations


Aug 13, 2019
Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler


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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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Distributed Learning over Unreliable Networks


Oct 17, 2018
Hanlin Tang, Chen Yu, Cedric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu, Ce Zhang


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SparCML: High-Performance Sparse Communication for Machine Learning


Oct 02, 2018
Cédric Renggli, Dan Alistarh, Torsten Hoefler, Mehdi Aghagolzadeh


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The Convergence of Sparsified Gradient Methods


Sep 27, 2018
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli

* NIPS 2018 - Advances in Neural Information Processing Systems; Authors in alphabetic order 

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The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory


Jun 22, 2018
Dan Alistarh, Christopher De Sa, Nikola Konstantinov

* To be published in PoDC 2018; 18 pages, 1 figure; Changes: added pseudocode for Algorithm 2, some references and corrected typos 

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Compressive Sensing with Low Precision Data Representation: Theory and Applications


Jun 06, 2018
Nezihe Merve Gürel, Kaan Kara, Alen Stojanov, Tyler Smith, Dan Alistarh, Markus Püschel, Ce Zhang

* 33 pages, 9 figures 

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