1 citations · 2 across the 3 of their papers we have counts for
5 papers
Haphazard Inputs as Images in Online Learning
Rohit Agarwal, Aryan Dessai, Arif Ahmed Sekh +3
The field of varying feature space in online learning settings, also known as haphazard inputs, is very prominent nowadays due to its applicability in various fields. However, the…
packetLSTM: Dynamic LSTM Framework for Streaming Data with Varying Feature Space
Rohit Agarwal, Karaka Prasanth Naidu, Alexander Horsch +2
We study the online learning problem characterized by the varying input feature space of streaming data. Although LSTMs have been employed to effectively capture the temporal natur…
Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis
Rohit Agarwal, Arijit Das, Alexander Horsch +2
The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption…
Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols
Iqra Qasim, Alexander Horsch, Dilip K. Prasad
Untrimmed videos have interrelated events, dependencies, context, overlapping events, object-object interactions, domain specificity, and other semantics that are worth highlightin…
No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series
Rohit Agarwal, Aman Sinha, Ayan Vishwakarma +5
Modeling irregularly-sampled time series (ISTS) is challenging because of missing values. Most existing methods focus on handling ISTS by converting irregularly sampled data into r…