Rohit Agarwal
Deepak Kumar Gupta,
Udbhav Bamba,
Abhishek Thakur,
Akash Gupta,
Rohit Agarwal,
Suraj Sharan
m.fl.:
An UltraMNIST classification benchmark to train CNNs for very large images
Rohit Agarwal,
Ludwig Alexander Horsch,
Krishna Agarwal,
Dilip Kumar Prasad
:
Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis
Rohit Agarwal,
Ludwig Alexander Horsch,
Krishna Agarwal,
Dilip Kumar Prasad
:
packetLSTM: Dynamic LSTM Framework for Streaming Data with Varying Feature Space
arXiv 2024 DOI
Gauri Arora,
Ankit Butola,
Ruchi Rajput,
Rohit Agarwal,
Krishna Agarwal,
Ludwig Alexander Horsch
m.fl.:
Taxonomy of hybridly polarized Stokes vortex beams
Himanshu Buckchash,
Momojit Biswas,
Rohit Agarwal,
Dilip Kumar Prasad
:
Hedging Is Not All You Need: A Simple Baseline for Online Learning Under Haphazard Inputs
arXiv 2024 DOI
Rohit Agarwal,
Ludwig Alexander Horsch,
Dilip Kumar Prasad
:
Modelling Irregularly Sampled Time Series Without Imputation
arXiv.org 2023 DOI
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,
Gyanendra Das,
Saksham Aggarwal,
Ludwig Alexander Horsch,
Dilip Kumar Prasad
:
Mabnet: Master Assistant Buddy Network With Hybrid Learning for Image Retrieval
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 ARKIV
Rohit Agarwal,
Dilip K. Prasad
:
Scalable Online Deep Learning for Streaming Data with Variable Feature Spaces: Architectures, Imparting Scalability and Data Transformation
UiT Norges arktiske universitet 12. november 2025
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
:
Haphazard Inputs: Handling Dimension Varying Inputs in an Online Setting
2023
Rohit Agarwal
:
In-context Learning, Finetuning and RLHF in LLMs
2023
Rohit Agarwal,
Krishna Agarwal,
Alexander Horsch,
Dilip K. Prasad
:
Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs
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