| Multitask Prompted Training Enables Zero-Shot Task Generalization V Sanh, A Webson, C Raffel, S Bach, L Sutawika, Z Alyafeai, A Chaffin, ... International Conference on Learning Representations, 2022 | 1226 | 2022 |
| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1134 | 2023 |
| A survey on federated learning for resource-constrained iot devices A Imteaj, U Thakker, S Wang, J Li, MH Amini IEEE Internet of Things Journal 9 (1), 1-24, 2021 | 368 | 2021 |
| Benchmarking TinyML Systems: Challenges and Direction CR Banbury, VJ Reddi, M Lam, W Fu, A Fazel, J Holleman, X Huang, ... MLSys 2020 Workshop, 2020 | 254* | 2020 |
| PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts SH Bach, V Sanh, ZX Yong, A Webson, C Raffel, NV Nayak, A Sharma, ... ACL Demos 2022, 2022 | 238 | 2022 |
| MicroNets: Neural network architectures for deploying TinyML applications on commodity microcontrollers C Banbury, C Zhou, I Fedorov, R Matas, U Thakker, D Gope, ... Proceedings of Machine Learning and Systems 3, 2021 | 222 | 2021 |
| Mlperf tiny benchmark C Banbury, VJ Reddi, P Torelli, J Holleman, N Jeffries, C Kiraly, P Montino, ... NeurIPS 2021, 2021 | 157 | 2021 |
| Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art A Imteaj, K Mamun Ahmed, U Thakker, S Wang, J Li, MH Amini Federated and Transfer Learning 27, 7-27, 2023 | 50 | 2023 |
| Compressing RNNs to kilobyte budget for IoT devices using Kronecker products U Thakker, I Fedorov, C Zhou, D Gope, M Mattina, G Dasika, J Beu ACM Journal on Emerging Technologies in Computing Systems (JETC) 17 (4), 1-18, 2021 | 49* | 2021 |
| Run-Time Efficient RNN Compression for Inference on Edge Devices U Thakker, J Beu, D Gope, G Dasika, M Matthew 4th edition of Workshop on Energy Efficient Machine Learning and Cognitive …, 2019 | 29 | 2019 |
| Skipping rnn state updates without retraining the original model J Tao, U Thakker, G Dasika, J Beu Proceedings of the 1st Workshop on Machine Learning on Edge in Sensor …, 2019 | 19 | 2019 |
| Ternary MobileNets via Per-Layer Hybrid Filter Banks D Gope, J Beu, U Thakker, M Mattina CVPR Workshop 2020, 2019 | 18 | 2019 |
| Measuring scheduling efficiency of RNNs for NLP applications U Thakker, J Beu, G Dasika, M Mattina 6th edition of International Workshop on Performance Analysis of Machine …, 2019 | 17 | 2019 |
| Pushing the limits of RNN Compression U Thakker, I Fedorov, J Beu, D Gope, C Zhou, G Dasika, M Mattina 5th edition of Workshop on Energy Efficient Machine Learning and Cognitive …, 2019 | 16 | 2019 |
| A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning N Ardalani, U Thakker, A Albarghouthi, K Sankaralingam 2nd International Workshop on AI-assisted Design for Architecture co-located …, 2019 | 15 | 2019 |
| Compressing Language Models using Doped Kronecker Products U Thakker, P Whatmough, M Mattina, J Beu On Device Intelligence Workshop at Third Conference on Machine Learning and …, 2020 | 14 | 2020 |
| Rank and run-time aware compression of NLP Applications U Thakker, J Beu, D Gope, G Dasika, M Mattina Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language …, 2020 | 11 | 2020 |
| Doping: A technique for extreme compression of lstm models using sparse structured additive matrices U Thakker, P Whatmough, Z Liu, M Mattina, J Beu Proceedings of machine learning and systems 3, 533-549, 2021 | 10* | 2021 |
| Understanding the impact of dynamic channel pruning on conditionally parameterized convolutions R Raju, D Gope, U Thakker, J Beu Proceedings of the 2nd International Workshop on Challenges in Artificial …, 2020 | 7 | 2020 |
| Aggressive Compression of MobileNets Using Hybrid Ternary Layers D Gope, J Beu, U Thakker, M Mattina 2nd edition of the tinyML Summit, 2020 | 6 | 2020 |
Matthew MattinaVerified email at alumni.princeton.edu
