Power delivery for series connected voltage domains in digital circuits. - Pradeep S. Shenoy; Igor Fedorov; Tyler Neyens; Philip T. Krein (ICEAC, 2011)

Robust Bayesian method for simultaneous block sparse signal recovery with applications to face recognition. - Igor Fedorov; Ritwik Giri; Bhaskar D. Rao; Truong Q. Nguyen (ICIP, 2016)

Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem. - Alican Nalci; Igor Fedorov; Bhaskar D. Rao (CoRR, 2016)

Spect image reconstruction under imaging time constraints. - Igor Fedorov; Sebastian Obrzut; Bongyong Song; Bhaskar D. Rao (ACSSC, 2017)

Multimodal sparse Bayesian dictionary learning applied to multimodal data classification. - Igor Fedorov; Bhaskar D. Rao; Truong Q. Nguyen (ICASSP, 2017)

Evaluating Coverage Effectiveness of Multi-Camera Domes Placement for Volumetric Surveillance. - Hiba H. Alqaysi; Najeem Lawal; Igor Fedorov; Mattias O'Nils (ICDSC, 2017)

Placement Strategy of Multi-Camera Volumetric Surveillance System for Activities Monitoring. - Igor Fedorov; Najeem Lawal; Mattias O'Nils; Hiba H. Alqaysi (ICDSC, 2017)

Relevance Subject Machine: A Novel Person Re-identification Framework. - Igor Fedorov; Ritwik Giri; Bhaskar D. Rao; Truong Q. Nguyen (CoRR, 2017)

Full Coverage Optimization for Multi Camera Dome Placement in Volumetric Monitoring. - Hiba H. Alqaysi; Najeem Lawal; Igor Fedorov; Mattias O'Nils (ICDSC, 2018)

Towards calibration of outdoor multi-camera visual monitoring system. - Igor Fedorov; Najeem Lawal; Benny Thörnberg; Hiba H. Alqaysi; Mattias O'Nils (ICDSC, 2018)

Re-Weighted Learning for Sparsifying Deep Neural Networks. - Igor Fedorov; Bhaskar D. Rao (CoRR, 2018)

Multimodal Sparse Bayesian Dictionary Learning. - Igor Fedorov; Bhaskar D. Rao (CoRR, 2018)

A unified framework for sparse non-negative least squares using multiplicative updates and the non-negative matrix factorization problem. - Igor Fedorov; Alican Nalci; Ritwik Giri; Bhaskar D. Rao; Truong Q. Nguyen; Harinath Garudadri (Signal Process., 2018)

Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem. - Alican Nalci; Igor Fedorov; Maher Al-Shoukairi; Thomas T. Liu; Bhaskar D. Rao (IEEE Trans. Signal Process., 2018)

Structured Learning with Scale Mixture Priors. - Igor Fedorov (2018)

Design Exploration of Multi-Camera Dome. - Hiba H. Alqaysi; Najeem Lawal; Igor Fedorov; Benny Thörnberg; Mattias O'Nils (ICDSC, 2019)

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. - Igor Fedorov; Ryan P. Adams; Matthew Mattina; Paul N. Whatmough (NeurIPS, 2019)

Pushing the limits of RNN Compression. - Urmish Thakker; Igor Fedorov; Jesse G. Beu; Dibakar Gope; Chu Zhou; Ganesh Dasika; Matthew Mattina (EMC2@NeurIPS, 2019)

Compressing RNNs for IoT devices by 15-38x using Kronecker Products. - Urmish Thakker; Jesse G. Beu; Dibakar Gope; Chu Zhou; Igor Fedorov; Ganesh Dasika; Matthew Mattina (CoRR, 2019)

SSGD: Sparsity-Promoting Stochastic Gradient Descent Algorithm for Unbiased Dnn Pruning. - Ching Hua Lee; Igor Fedorov; Bhaskar D. Rao; Harinath Garudadri (ICASSP, 2020)

Mango: A Python Library for Parallel Hyperparameter Tuning. - Sandeep Singh Sandha; Mohit Aggarwal; Igor Fedorov; Mani B. Srivastava (ICASSP, 2020)

TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. - Igor Fedorov; Marko Stamenovic; Carl Jensen; Li-Chia Yang; Ari Mandell; Yiming Gan; Matthew Mattina; Paul N. Whatmough (INTERSPEECH, 2020)

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. - Colby R. Banbury; Chuteng Zhou; Igor Fedorov; Ramon Matas Navarro; Urmish Thakker; Dibakar Gope; Vijay Janapa Reddi; Matthew Mattina; Paul N. Whatmough (MLSys, 2021)

Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products. - Urmish Thakker; Igor Fedorov; Chu Zhou; Dibakar Gope; Matthew Mattina; Ganesh Dasika; Jesse G. Beu (ACM J. Emerg. Technol. Comput. Syst., 2021)

A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms. - Hiba H. Alqaysi; Igor Fedorov; Faisal Z. Qureshi; Mattias O'Nils (J. Imaging, 2021)

Spontaneous and Stimulated Undulator Radiation in Symmetric and Asymmetric Multi-Periodic Magnetic Fields. - Konstantin V. Zhukovsky; Igor Fedorov (Symmetry, 2021)

A Two-Layer 3-D Reconstruction Method and Calibration for Multicamera-Based Volumetric Positioning and Characterization. - Igor Fedorov; Benny Thörnberg; Hiba H. Alqaysi; Najeem Lawal; Mattias O'Nils (IEEE Trans. Instrum. Meas., 2021)

UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. - Igor Fedorov; Ramon Matas Navarro; Hokchhay Tann; Chuteng Zhou; Matthew Mattina; Paul N. Whatmough (NeurIPS, 2022)

Magnitude-Aware Probabilistic Speaker Embeddings. - Nikita Kuzmin; Igor Fedorov; Alexey Sholokhov (Odyssey, 2022)

UDC: Unified DNAS for Compressible TinyML Models. - Igor Fedorov; Ramon Matas Navarro; Hokchhay Tann; Chuteng Zhou; Matthew Mattina; Paul N. Whatmough (CoRR, 2022)

Restructurable Activation Networks. - Kartikeya Bhardwaj; James Ward; Caleb Tung; Dibakar Gope; Lingchuan Meng; Igor Fedorov; Alex Chalfin; Paul N. Whatmough; Danny Loh (CoRR, 2022)

Beam Asymmetry, Divergence and Energy Spread Effects on the Radiation from Planar Undulators. - Konstantin V. Zhukovsky; Igor Fedorov (Symmetry, 2022)

Efficient Edge Inference by Selective Query. - Anil Kag; Igor Fedorov; Aditya Gangrade; Paul N. Whatmough; Venkatesh Saligrama (ICLR, 2023)

PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices. - Yuji Chai; Devashree Tripathy; Chuteng Zhou; Dibakar Gope; Igor Fedorov; Ramon Matas Navarro; David Brooks 001; Gu-Yeon Wei; Paul N. Whatmough (CoRR, 2023)

SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape. - Hua Zheng; Kuang-Hung Liu; Igor Fedorov; Xin Zhang 054; Wen-Yen Chen; Wei Wen (CoRR, 2023)

DistDNAS: Search Efficient Feature Interactions within 2 Hours. - Tunhou Zhang; Wei Wen; Igor Fedorov; Xi Liu; Buyun Zhang; Fangqiu Han; Wen-Yen Chen; Yiping Han; Feng Yan 001; Hai Li 001; Yiran Chen 001 (IEEE Big Data, 2024)

DεpS: Delayed ε-Shrinking for Faster Once-for-All Training. - Aditya Annavajjala; Alind Khare; Animesh Agrawal; Igor Fedorov; Hugo Latapie; Myungjin Lee; Alexey Tumanov (ECCV (89), 2024)

MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases. - Zechun Liu; Changsheng Zhao 002; Forrest N. Iandola; Chen Lai; Yuandong Tian; Igor Fedorov; Yunyang Xiong; Ernie Chang; Yangyang Shi; Raghuraman Krishnamoorthi; Liangzhen Lai; Vikas Chandra (ICML, 2024)

SiGeo: Sub-One-Shot NAS via Geometry of Loss Landscape. - Hua Zheng; Kuang-Hung Liu; Igor Fedorov; Xin Zhang 054; Wen-Yen Chen; Wei Wen (KDD, 2024)

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale. - Wei Wen; Kuang-Hung Liu; Igor Fedorov; Xin Zhang 054; Hang Yin 005; Weiwei Chu; Kaveh Hassani; Mengying Sun; Jiang Liu 011; Xu Wang; Lin Jiang; Yuxin Chen 001; et al. (WWW (Companion Volume), 2024)

DεS: Delayed ε-Shrinking for Faster Once-For-All Training. - Aditya Annavajjala; Alind Khare; Animesh Agrawal; Igor Fedorov; Hugo Latapie; Myungjin Lee; Alexey Tumanov (CoRR, 2024)

Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversations. - Igor Fedorov; Kate Plawiak; Lemeng Wu; Tarek Elgamal; Naveen Suda; Eric Smith; Hongyuan Zhan; Jianfeng Chi; Yuriy Hulovatyy; Kimish Patel; Zechun Liu; Changsheng Zhao 002; et al. (CoRR, 2024)

SpinQuant: LLM Quantization with Learned Rotations. - Zechun Liu; Changsheng Zhao 002; Igor Fedorov; Bilge Soran; Dhruv Choudhary; Raghuraman Krishnamoorthi; Vikas Chandra; Yuandong Tian; Tijmen Blankevoort (ICLR, 2025)

∇QDARTS: Quantization as an Elastic Dimension to Differentiable NAS. - Payman Behnam; Uday Kamal; Sanjana Vijay Ganesh; Zhaoyi Li; Michael Jurado; Alind Khare; Igor Fedorov; Gaowen Liu; Alexey Tumanov (Trans. Mach. Learn. Res., 2025)