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A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation



Wenjing Yang , Ganghua Wang , Enmao Diao , Vahid Tarokh , Jie Ding , Yuhong Yang


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Inference and Sampling for Archimax Copulas



Yuting Ng , Ali Hasan , Vahid Tarokh


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Multi-Agent Adversarial Attacks for Multi-Channel Communications



Juncheng Dong , Suya Wu , Mohammadreza Sultani , Vahid Tarokh


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On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections



Mohammadreza Soltani , Suya Wu , Yuerong Li , Jie Ding , Vahid Tarokh


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Toward Data-Driven STAP Radar



Shyam Venkatasubramanian , Chayut Wongkamthong , Mohammadreza Soltani , Bosung Kang , Sandeep Gogineni , Ali Pezeshki , Muralidhar Rangaswamy , Vahid Tarokh

* 5 pages, 4 figures. Submitted to 2022 IEEE Radar Conference (RadarConf) 

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A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow



Mohammadreza Momenifar , Enmao Diao , Vahid Tarokh , Andrew D. Bragg

* this article is a conference version of arXiv:2103.01074 

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Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models



Mohammadreza Momenifar , Enmao Diao , Vahid Tarokh , Andrew D. Bragg

* AI2ASE: AAAI Workshop on AI to Accelerate Science and Engineering, 2022 

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Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions



Juncheng Dong , Simiao Ren , Yang Deng , Omar Khatib , Jordan Malof , Mohammadreza Soltani , Willie Padilla , Vahid Tarokh


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