Rohit Agarwal


PhD fellow


  • Rohit Agarwal, Ankit Butola, Ludwig Alexander Horsch, Dilip Kumar Prasad, Krishna Agarwal :
    Taxonomy of hybridly polarized Stokes vortex beams
    arXiv.org 2023 DOI
  • Rohit Agarwal, Ludwig Alexander Horsch, Dilip Kumar Prasad :
    Modelling Irregularly Sampled Time Series Without Imputation
    arXiv.org 2023 DOI
  • Rohit Agarwal, Gyanendra Das, Saksham Aggarwal, Ludwig Alexander Horsch, Dilip Kumar Prasad :
    Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval
    Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2023 DOI
  • Rohit Agarwal, Dilip Kumar Prasad, Ludwig Alexander Horsch, Deepak Kumar Gupta :
    Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
    Transactions on Machine Learning Research (TMLR) 2023
  • Rohit Agarwal :
    Haphazard Inputs: Handling Dimension Varying Inputs in an Online Setting
    2023
  • Rohit Agarwal :
    Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
    2023
  • Rohit Agarwal, Ludwig Alexander Horsch, Dilip Kumar Prasad :
    Code - Modelling Irregularly Sampled Time Series Without Imputation
    2023
  • Rohit Agarwal, Ludwig Alexander Horsch, Dilip Kumar Prasad :
    Code - MABNET: Master Assistant Buddy Network for Image Retrieval
    2023
  • Rohit Agarwal :
    Video - Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval
    2023
  • Rohit Agarwal :
    Haphazard Inputs: Handling Dimension Varying Inputs in an Online Setting
    2023
  • Rohit Agarwal :
    In-context Learning, Finetuning and RLHF in LLMs​
    2023
  • Rohit Agarwal :
    Code - Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
    2023
  • Rohit Agarwal, Krishna Agarwal, Alexander Horsch, Dilip K. Prasad :
    Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs
    2022 ARKIV
  • Rohit Agarwal :
    Video - Auxiliary Network: Scalable and Agile Online Learning for Dynamic System with Inconsistently Available Inputs
    2022

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