Sparse Communication for Training Deep Networks

Sep 19, 2020
Negar Foroutan Eghlidi, Martin Jaggi

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Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Aug 08, 2020
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

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PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning

Aug 04, 2020
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

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Multi-Head Attention: Collaborate Instead of Concatenate

Jun 29, 2020
Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi

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Taming GANs with Lookahead

Jun 25, 2020
Tatjana Chavdarova, Matteo Pagliardini, Martin Jaggi, Francois Fleuret

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Byzantine-Robust Learning on Heterogeneous Datasets via Resampling

Jun 23, 2020
Lie He, Sai Praneeth Karimireddy, Martin Jaggi

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Dynamic Model Pruning with Feedback

Jun 12, 2020
Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi

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* appearing at ICLR 2020 

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Ensemble Distillation for Robust Model Fusion in Federated Learning

Jun 12, 2020
Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi

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Extrapolation for Large-batch Training in Deep Learning

Jun 10, 2020
Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi

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Secure Byzantine-Robust Machine Learning

Jun 08, 2020
Lie He, Sai Praneeth Karimireddy, Martin Jaggi

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Masking as an Efficient Alternative to Finetuning for Pretrained Language Models

Apr 26, 2020
Mengjie Zhao, Tao Lin, Martin Jaggi, Hinrich Schütze

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Data Parallelism in Training Sparse Neural Networks

Mar 25, 2020
Namhoon Lee, Philip H. S. Torr, Martin Jaggi

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* ICLR 2020 workshop on PML4DC: Learning under limited/low resource scenarios 

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A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

Mar 23, 2020
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich

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Robust Cross-lingual Embeddings from Parallel Sentences

Dec 28, 2019
Ali Sabet, Prakhar Gupta, Jean-Baptiste Cordonnier, Robert West, Martin Jaggi

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Advances and Open Problems in Federated Learning

Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

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On the Relationship between Self-Attention and Convolutional Layers

Nov 08, 2019
Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi

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On the Tunability of Optimizers in Deep Learning

Oct 25, 2019
Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret

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* Under review at ICLR 2020. 16 pages, 8 figures 

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Model Fusion via Optimal Transport

Oct 12, 2019
Sidak Pal Singh, Martin Jaggi

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* Accepted at NeurIPS 2019: Optimal Transport & Machine Learning workshop 

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Decentralized Deep Learning with Arbitrary Communication Compression

Jul 22, 2019
Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi

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Correlating Twitter Language with Community-Level Health Outcomes

Jun 24, 2019
Arno Schneuwly, Ralf Grubenmann, Séverine Rion Logean, Mark Cieliebak, Martin Jaggi

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* ACL SMM4H Workshop (Social Media Mining for Health Applications) 

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PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

May 31, 2019
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

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On Linear Learning with Manycore Processors

May 03, 2019
Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel

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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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Better Word Embeddings by Disentangling Contextual n-Gram Information

Apr 10, 2019
Prakhar Gupta, Matteo Pagliardini, Martin Jaggi

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* NAACL 2019 

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Crosslingual Document Embedding as Reduced-Rank Ridge Regression

Apr 08, 2019
Martin Josifoski, Ivan S. Paskov, Hristo S. Paskov, Martin Jaggi, Robert West

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* In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19) 

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Overcoming Multi-Model Forgetting

Mar 02, 2019
Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires, Martin Jaggi, Anthony Davison, Mathieu Salzmann, Claudiu Musat

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Structure Tree-LSTM: Structure-aware Attentional Document Encoders

Feb 26, 2019
Khalil Mrini, Claudiu Musat, Michael Baeriswyl, Martin Jaggi

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