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Urmish Thakker
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2020 – today
- 2024
- [i21]Zoltan Csaki, Bo Li, Jonathan Li, Qiantong Xu, Pian Pawakapan, Leon Zhang, Yun Du, Hengyu Zhao, Changran Hu, Urmish Thakker:
SambaLingo: Teaching Large Language Models New Languages. CoRR abs/2404.05829 (2024) - [i20]Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi, Yun Du, Mingran Wang, Xiangyu Song, Kejie Zhang, Tianren Gao, Angela Wang, Karen Li, Yongning Sheng, Joshua Brot, Denis Sokolov, Apurv Vivek, Calvin Leung, Arjun Sabnis, Jiayu Bai, Tuowen Zhao, Mark Gottscho, David Jackson, Mark Luttrell, Manish K. Shah, Edison Chen, Kaizhao Liang, Swayambhoo Jain, Urmish Thakker, Dawei Huang, Sumti Jairath, Kevin J. Brown, Kunle Olukotun:
SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts. CoRR abs/2405.07518 (2024) - [i19]Ravi Raju, Swayambhoo Jain, Bo Li, Jonathan Li, Urmish Thakker:
Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge. CoRR abs/2408.08808 (2024) - [i18]Xueliang Zhao, Lin Zheng, Haige Bo, Changran Hu, Urmish Thakker, Lingpeng Kong:
SubgoalXL: Subgoal-based Expert Learning for Theorem Proving. CoRR abs/2408.11172 (2024) - 2023
- [i17]Venkat Srinivasan, Darshan Gandhi, Urmish Thakker, Raghu Prabhakar:
Training Large Language Models Efficiently with Sparsity and Dataflow. CoRR abs/2304.05511 (2023) - [i16]Zoltan Csaki, Pian Pawakapan, Urmish Thakker, Qiantong Xu:
Efficiently Adapting Pretrained Language Models To New Languages. CoRR abs/2311.05741 (2023) - 2022
- [j2]Ahmed Imteaj
, Urmish Thakker, Shiqiang Wang
A Survey on Federated Learning for Resource-Constrained IoT Devices. IEEE Internet Things J. 9(1): 1-24 (2022) - [c12]Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. ACL (demo) 2022: 93-104 - [c11]Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush:
Multitask Prompted Training Enables Zero-Shot Task Generalization. ICLR 2022 - [i15]Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Févry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Mike Tian-Jian Jiang, Alexander M. Rush:
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. CoRR abs/2202.01279 (2022) - 2021
- [j1]Urmish Thakker, Igor Fedorov, Chu Zhou, Dibakar Gope, Matthew Mattina, Ganesh Dasika, Jesse G. Beu:
Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products. ACM J. Emerg. Technol. Comput. Syst. 17(4): 46:1-46:18 (2021) - [c10]Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough:
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. MLSys 2021 - [c9]Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu:
Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices. MLSys 2021 - [c8]Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Nat Jeffries, Csaba Király, Jeremy Holleman, Pietro Montino, David Kanter, Pete Warden, Danilo Pau, Urmish Thakker, Antonio Torrini, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu:
MLPerf Tiny Benchmark. NeurIPS Datasets and Benchmarks 2021 - [i14]Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu:
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices. CoRR abs/2102.07071 (2021) - [i13]Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Király, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker, Antonio Torrini, Pete Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu:
MLPerf Tiny Benchmark. CoRR abs/2106.07597 (2021) - [i12]Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey
Multitask Prompted Training Enables Zero-Shot Task Generalization. CoRR abs/2110.08207 (2021) - 2020
- [c7]Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina:
Ternary MobileNets via Per-Layer Hybrid Filter Banks. CVPR Workshops 2020: 3036-3046 - [c6]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Rank and run-time aware compression of NLP Applications. SustaiNLP@EMNLP 2020: 8-18 - [c5]Xueqin Huang, Urmish Thakker, Dibakar Gope, Jesse G. Beu:
Pushing the Envelope of Dynamic Spatial Gating technologies. AIChallengeIoT@SenSys 2020: 21-26 - [c4]Ravi Raju, Dibakar Gope, Urmish Thakker, Jesse G. Beu:
Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions. AIChallengeIoT@SenSys 2020: 27-33 - [i11]Urmish Thakker, Paul N. Whatmough, Matthew Mattina, Jesse G. Beu:
Compressing Language Models using Doped Kronecker Products. CoRR abs/2001.08896 (2020) - [i10]Ahmed Imteaj
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art. CoRR abs/2002.10610 (2020) - [i9]Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David A. Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav:
Benchmarking TinyML Systems: Challenges and Direction. CoRR abs/2003.04821 (2020) - [i8]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Rank and run-time aware compression of NLP Applications. CoRR abs/2010.03193 (2020) - [i7]Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough:
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. CoRR abs/2010.11267 (2020)
2010 – 2019
- 2019
- [c3]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Run-Time Efficient RNN Compression for Inference on Edge Devices. EMC2@HPCA/CVPR/ISCA 2019: 26-30 - [c2]Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina:
Pushing the limits of RNN Compression. EMC2@NeurIPS 2019: 18-21 - [c1]Jin Tao, Urmish Thakker
Skipping RNN State Updates without Retraining the Original Model. SenSys-ML 2019: 31-36 - [i6]Urmish Thakker
Measuring scheduling efficiency of RNNs for NLP applications. CoRR abs/1904.03302 (2019) - [i5]Urmish Thakker
Compressing RNNs for IoT devices by 15-38x using Kronecker Products. CoRR abs/1906.02876 (2019) - [i4]Urmish Thakker
Run-Time Efficient RNN Compression for Inference on Edge Devices. CoRR abs/1906.04886 (2019) - [i3]Newsha Ardalani, Urmish Thakker
A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning. CoRR abs/1906.07840 (2019) - [i2]Urmish Thakker
Pushing the limits of RNN Compression. CoRR abs/1910.02558 (2019) - [i1]Dibakar Gope, Jesse G. Beu, Urmish Thakker
Ternary MobileNets via Per-Layer Hybrid Filter Banks. CoRR abs/1911.01028 (2019)
Coauthor Index
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