J. Dong, S. Ren, Y. Deng, O. Khatib, J. Malof, M. Soltani, W. Padilla, V. Tarokh, “Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions”, International Conference on Learning Representations (ICLR), April 2022.
C. Le, J. Dong, M. Soltani, V. Tarokh, “Task Affinity with Maximum Bipartite Matching in Few-Shot Learning”, International Conference on Learning Representations (ICLR), April 2022.
M.Soltani, S. Wu, J. Ding, V. Tarokh, “On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections”, International Conference on The Data Compression Conference (DCC), March 2022.
S. Venkatasubramanian, C. Wongkamthong, M. Soltani, B. Kang, S. Gogineni, A. Pezeshki, M. Rangaswamy, V. Tarokh, “Toward Data-Driven STAP Radar”, IEEE Radar Conference, March 2022.
Dong, S. Wu, M. Soltani, V. Tarokh, “Multi-Agent Adversarial Attacks for Multi-Channel Communication”, International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2022.
Y. Deng, J. Dong, S. Ren, O. Khatib, M. Soltani, V. Tarokh, W. Padilla, J. Malof, “Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials”, NeurIPS Datasets and Benchmarks Track, May 2021.
C. Le, M. Soltani, J. Dong, V. Tarokh, “Fisher Task Distance and Its Applications in Transfer Learning and Neural Architecture Search”, submitted, 2021.
C. Le, M. Soltani, R. Ravier, V. Tarokh, “Neural Architecture Search From Task Similarity Measure”, submitted, 2021.
M. Angjelichinoski, M. Soltani, J. Choi, B. Pesaran, V. Tarokh, “Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions from Limited Data” , IEEE Transactions on Neural Systems & Rehabilitation Engineering (TNSRE), 2021.
Y.Feng, C. Wongkamthong, M. Soltani, Y. NG, S. Gogineni, B. Kang, A. Pezeshki, R. Calderbank, M. Rangaswamy, V. Tarokh, “Knowledge-Aided Data-DrivenRadar Clutter Cancellation”, IEEE Radar Conference, May, 2021.
M. Cho, M.Soltani, C. Hegde, “One-Shot Neural Architecture Search via Compressive Sensing”, ICLR Workshop on Neural Architecture Search (NAS), May, 2021.
C. Cannella, M. Soltani, V. Tarokh, “Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows’’, International Conference on Learning Representation (ICLR), May 2021.
C. Le, M.Soltani, R. Ravier, V. Tarokh, “Task-Aware Neural Architecture Search”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), June 2021.
Y.Feng, C. Wongkamthong, M. Soltani, Y. NG, S. Gogineni, B. Kang, A. Pezeshki, R. Calderbank, M. Rangaswamy, V. Tarokh, “Knowledge-Aided Data-DrivenRadar Clutter Cancellation”, IEEE Radar Conference, May, 2021.
M.Soltani, S. Wu, Y.Li, R. Ravier, J. Ding, V. Tarokh, “Compressing Deep Networks Using Fisher Score of Feature Maps”, International Conference on The Data Compression Conference (DCC), March 2021.
M.Soltani, S. Wu, J. Ding, R. Ravier, V. Tarokh, “On the Information of Feature Maps and Pruning of Deep Neural Networks”, International Conference on Pattern Recognition (ICPR), Jan 2021.
M. Angjelichinoski, M. Soltani, J. Choi, B. Pesaran, V. Tarokh, “Deep James-Stien Neural Networks for Brain-Computer Interfaces”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020.
C. Cannella, J. Ding, M. Soltani, Y. Zhou, V. Tarokh, “Perception-Distortion Trade-Off with Restricted Boltzmann Machines”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020.
M.Soltani, S. Jain, C. Hegde, “Learning Structured Signals Using GANs with Applications in Denoising and Demixing”, Asilomar Conference on Signals, Systems, and Computers, Nov 2019.
M.Soltani, S. Jain, A. Sambasivan, “Unsupervised Demixing of Structured Signals from Their Superposition Using GANs”, ICLR Workshop on Deep Generative Models for Highly Structured Data, May 2019.
M. Soltani and C. Hegde, “Fast and Provable Algorithms for Learning Two-Layer Polynomial Neural Networks”, IEEE Transactions on Signal Processing (TSP), vol. 67, no. 13, p3361-3371, July 2019.
M. Soltani and C. Hegde, “Fast Low-Rank Estimation for Ill-Conditioned Matrices’’, International Symposium on Information Theory (ISIT), June 2018.
M. Soltani and C. Hegde, “Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation”, Artificial Intelligence and Statistics (AISTAT), April 2018 (acceptance rate: %33).
M. Soltani and C. Hegde, “Fast Low-Rank Matrix Estimation without the Condition Number”, https://arxiv.org/abs/1712.03281, Dec 2017.
M. Soltani and C. Hegde, “Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation”, NIPS Workshop On Deep Learning: Bridging Theory and Practice (DLP), Dec 2017.
M.Soltani and C. Hegde, “Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations”, IEEE GlobalSIP Symposium on Sparse Signal Processing and Deep Learning, Nov 2017.
V. Shah, M.Soltani and C. Hegde, “Reconstruction from Periodic Nonlinearities, with Applications to HDR Imaging”, Asilomar Conference on Signals, Systems, and Computers, Nov 2017.
M.Soltani and C. Hegde, “Fast Algorithms for Learning Latent Variables in Graphical Models”, ACM KDD Mining and Learning With Graphs (KDD MLG), Aug 2017.
M.Soltani and C. Hegde, Improved Algorithms for Matrix Recovery from Rank-One Projections, poster presentation in Midwest Machine Learning Symposium (MMLS), May 2017 (Winner of the best poster award).
M.Soltani and C. Hegde, “Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations”, IEEE Transactions on Signal Processing (TSP), vol. 65, no. 16, p4209-4222, Aug 2017.
M.Soltani and C. Hegde, “Stable Recovery of Sparse Vectors From Random Sinusoidal Feature Maps”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017.
M. Soltani and C. Hegde, “Iterative Thresholding for Demixing Structured Superpositions in High Dimensions”, NIPS Workshop on Learning in High Dimensions with Structure (LHDS), Dec 2016 (Oral presentation; acceptance rate: 2/50).
M. Soltani and C. Hegde, “A Fast Iterative Algorithm for Demixing Sparse Signals from Nonlinear Observations”, IEEE GlobalSIP Symposium on Compressed Sensing and Deep Learning, Dec 2016.
M. Soltani and C. Hegde, “Demixing Sparse Signals from Nonlinear Observations,” Asilomar Conference on Signals, Systems, and Computers, Nov 2016.
M. Soltani, M. Hempel, and H. Sharif, “Utilization of Convex Optimization for Data Fusion-driven Sensor Management in WSNs”, International Wireless Communications \& Mobile Computing Conference (IWCMC), 2015.
M. Soltani, M. Hempel, and H. Sharif, “Data Fusion Utilization for optimizing Large-Scale Wireless Sensor Networks”, International Conference on Communications (ICC), 2014.
M. Maadani, S. A. Motamedi, and M. Soltani, “EDCA Delay Analysis of Spatial Multiplexing in IEEE802. 11-Based Wireless Sensor and Actuator Networks”, International Journal of Information and Electronics Engineering, 2(3), p.318, 2012.
M.Soltani, “A novel Tunable Opportunistic Routing Protocol for WSN Applications”, Amirkabir University of Technology, Technical Report, 2012.
M. Maadani, S. A. Motamedi, and M. Soltani, “Delay Analysis of MIMO-Enabled IEEE 802.11-Based Soft-Real-Time Wireless Sensor and Actuator Networks”, Dela, vol. 150, p200, 2011.
M. Soltani, S. A. Motamedi, S. Ahmadi, and M. Maadani, “Power-Aware and Void-Avoidant Routing Protocol for Reliable Industrial Wireless Sensor Networks”, International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), 2011.
