Thakker, Urmish

A Survey on Federated Learning for Resource-Constrained IoT Devices

Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 774

Multitask Prompted Training Enables Zero-Shot Task Generalization

Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 612

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

Zhang, Q., Hu, C., Upasani, S., Ma, B., Hong, F., Kamanuru, V., Rainton, J., Wu, C., Ji, M., Li, H., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 370

Benchmarking TinyML Systems: Challenges and Direction

Banbury, C. R., Reddi, V. J., Lam, M., Fu, W., Fazel, A., Holleman, J., Huang, X., Hurtado, R., Kanter, D., Lokhmotov, A., et al. (2020) · arXiv

IoT and Edge/Fog ComputingAdvanced Neural Network Applications

Cited by 216

Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers

Banbury, C., Zhou, C., Fedorov, I., Navarro, R. M., Thakker, U., Gope, D., Reddi, V. J., Mattina, M., & Whatmough, P. N. (2020) · arXiv

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 183

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, S., Sanh, V., Yong, Z. X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Natural Language Processing TechniquesTopic Modeling

Cited by 178

MLPerf Tiny Benchmark

Banbury, C., Reddi, V. J., Torelli, P., Holleman, J., Jeffries, N., Kiraly, C., Montino, P., Kanter, D., Ahmed, S., Pau, D., et al. (2021) · arXiv

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 121

Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art

Imteaj, A., Mamun Ahmed, K., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2023) · Adaptation, Learning, and Optimization

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 86

Compressing RNNs for IoT devices by 15-38x using Kronecker Products

Thakker, U., Beu, J., Gope, D., Zhou, C., Fedorov, I., Dasika, G., & Mattina, M. (2019) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 30

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 21

Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products

Thakker, U., Fedorov, I., Zhou, C., Gope, D., Mattina, M., Dasika, G., & Beu, J. (2021) · ACM Journal on Emerging Technologies in Computing Systems

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 8

Compressing Language Models using Doped Kronecker Products

Thakker, U., Whatmough, P. N., Liu, Z.-G., Mattina, M., & Beu, J. (2020) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 7

Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge

Raju, R., Jain, S., Li, B., Li, J., & Thakker, U. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

SETA: Scaling Environments for Terminal Agents

Shen, Q., Huang, Z., Kamanuru, V., Aliev, A., Rainton, J., Awelkair, A., Zeng, Z., Li, J., Dong, S., Yuan, Y., et al. (2026) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 0