1 citations · 2 across the 8 of their papers we have counts for
8 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…
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…
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…
Taxonomy of hybridly polarized Stokes vortex beams
Gauri Arora, Ankit Butola, Ruchi Rajput +5
Structured beams carrying topological defects, namely phase and Stokes singularities, have gained extensive interest in numerous areas of optics. The non-separable spin and orbital…
MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval
Rohit Agarwal, Gyanendra Das, Saksham Aggarwal +2
Image retrieval has garnered growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid…
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…