1 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Hedging Is Not All You Need: A Simple Baseline for Online Learning Under Haphazard Inputs
Himanshu Buckchash, Momojit Biswas, Rohit Agarwal +1
Handling haphazard streaming data, such as data from edge devices, presents a challenging problem. Over time, the incoming data becomes inconsistent, with missing, faulty, or new i…
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…
Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
Rohit Agarwal, Deepak Gupta, Alexander Horsch +1
Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the a…