Trevor Gale

According to our database, Trevor Gale authored at least 11 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Scorch: A Library for Sparse Deep Learning.

Bobby Yan, Alexander J. Root, Trevor Gale, David Broman, Fredrik Kjolstad

CoRR, 2024

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models.

Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz GUStavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas

CoRR, 2024

2023

JaxPruner: A concise library for sparsity research.

Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan S. Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci

CoRR, 2023

MegaBlocks: Efficient Sparse Training with Mixture-of-Experts.

Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

2021

On the Opportunities and Risks of Foundation Models.

Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, et al.

CoRR, 2021

2020

Sparse GPU kernels for deep learning.

Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen

Proceedings of the International Conference for High Performance Computing, 2020

Rigging the Lottery: Making All Tickets Winners.

Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen

Proceedings of the 37th International Conference on Machine Learning, 2020

Fast Sparse ConvNets.

Erich Elsen, Marat Dukhan, Trevor Gale, Karen Simonyan

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

The State of Sparsity in Deep Neural Networks.

Trevor Gale, Erich Elsen, Sara Hooker

CoRR, 2019

2017

Delineation of Skin Strata in Reflectance Confocal Microscopy Images with Recurrent Convolutional Networks.

Alican Bozkurt, Trevor Gale, Kivanç Köse, Christi Alessi-Fox, Dana H. Brooks, Milind Rajadhyaksha, Jennifer G. Dy

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

2016

dMath: Distributed Linear Algebra for DL.

Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, Trevor Gale

CoRR, 2016