| 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, 517-532, 2021 | 274 | 2021 |
| Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers I Fedorov, RP Adams, M Mattina, P Whatmough Advances in Neural Information Processing Systems 32, 2019 | 193 | 2019 |
| TinyLSTMs: Efficient neural speech enhancement for hearing aids I Fedorov, M Stamenovic, C Jensen, LC Yang, A Mandell, Y Gan, ... arXiv preprint arXiv:2005.11138, 2020 | 109 | 2020 |
| Mobilellm: Optimizing sub-billion parameter language models for on-device use cases Z Liu, C Zhao, F Iandola, C Lai, Y Tian, I Fedorov, Y Xiong, E Chang, ... arXiv preprint arXiv:2402.14905, 2024 | 49 | 2024 |
| Mango: A python library for parallel hyperparameter tuning SS Sandha, M Aggarwal, I Fedorov, M Srivastava ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 43 | 2020 |
| Compressing rnns for iot devices by 15-38x using kronecker products U Thakker, J Beu, D Gope, C Zhou, I Fedorov, G Dasika, M Mattina arXiv preprint arXiv:1906.02876, 2019 | 41 | 2019 |
| Automated Worker Activity Analysis in Indoor Environments for Direct-Work Rate Improvement from Long Sequences of RGB-D Images A Khosrowpour, I Fedorov, A Holynski, JC Niebles, M Golparvar-Fard Journal of Construction Engineering and Management, 2015 | 34 | 2015 |
| Automated Worker Activity Analysis in Indoor Environments for Direct-Work Rate Improvement from Long Sequences of RGB-D Images A Khosrowpour, I Fedorov, A Holynski, JC Niebles, M Golparvar-Fard Construction Research Congress 2014@ sConstruction in a Global Network, 729-738, 2014 | 34 | 2014 |
| UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units I Fedorov, R Matas, H Tann, C Zhou, M Mattina, P Whatmough Advances in Neural Information Processing Systems, 2022 | 27* | 2022 |
| Rectified Gaussian scale mixtures and the sparse non-negative least squares problem A Nalci, I Fedorov, M Al-Shoukairi, TT Liu, BD Rao IEEE Transactions on Signal Processing 66 (12), 3124-3139, 2018 | 24 | 2018 |
| SpinQuant--LLM quantization with learned rotations Z Liu, C Zhao, I Fedorov, B Soran, D Choudhary, R Krishnamoorthi, ... arXiv preprint arXiv:2405.16406, 2024 | 22 | 2024 |
| Power delivery for series connected voltage domains in digital circuits PS Shenoy, I Fedorov, T Neyens, PT Krein 2011 International Conference on Energy Aware Computing, 1-6, 2011 | 19 | 2011 |
| Pushing the limits of rnn compression U Thakker, I Fedorov, J Beu, D Gope, C Zhou, G Dasika, M Mattina 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive …, 2019 | 17 | 2019 |
| Robust Bayesian method for simultaneous block sparse signal recovery with applications to face recognition I Fedorov, R Giri, BD Rao, TQ Nguyen 2016 IEEE International Conference on Image Processing (ICIP), 3872-3876, 2016 | 15 | 2016 |
| Image denoising neural network architecture and method of training the same M El-Khamy, I Fedorov, J Lee US Patent 10,726,525, 2020 | 13 | 2020 |
| A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem I Fedorov, A Nalci, R Giri, BD Rao, TQ Nguyen, H Garudadri Signal Processing, 2018 | 13 | 2018 |
| Multimodal sparse Bayesian dictionary learning applied to multimodal data classification I Fedorov, BD Rao, TQ Nguyen 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 12 | 2017 |
| Restructurable activation networks K Bhardwaj, J Ward, C Tung, D Gope, L Meng, I Fedorov, A Chalfin, ... arXiv preprint arXiv:2208.08562, 2022 | 8 | 2022 |
| Multimodal sparse bayesian dictionary learning I Fedorov, BD Rao arXiv preprint arXiv:1804.03740, 2018 | 8 | 2018 |
| 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 | 6 | 2021 |
Paul N WhatmoughQualcomm AI Research, Harvard UniversityEmail verificata su qti.qualcomm.com
