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Creators/Authors contains: "Manekar, Raunak"

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  1. ; ; (, IEEE Transactions on Intelligent Transportation Systems)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; ; ; ; (, IEEE Transactions on Image Processing)
    Full Text Available 
  3. ; ; ; (, ICML workshop on ML Interpretability for Scientific Discovery)
    We consider the end-to-end deep learning approach for phase retrieval, a central problem in scientific imaging. We highlight a fundamental difficulty for learning that previous work has neglected, likely due to the biased datasets they use for training and evaluation. We propose a simple yet different formulation for PR that seems to overcome the difficulty and return consistently better qualitative results. 
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    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, ICML workshop on ML Interpretability for Scientific Discovery)
    In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such physical systems, i.e., solving the associated inverse problems, there is no unique solution. This causes fundamental difficulty in deploying the emerging end-to-end deep learning approach. Using the generalized phase retrieval problem as an illustrative example, we show that careful symmetry breaking on training data can help remove the difficulty and significantly improve the learning performance. We also extract and highlight the underlying mathematical principle of the proposed solution, which is directly applicable to other inverse problems. A full-length version of this paper can be found at https://arxiv.org/abs/2003.09077. 
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    Accepted Manuscript Full Text Available