118 citations · 130 across the 7 of their papers we have counts for
7 papers
DocHRL: A Hierarchical Reinforcement Learning Framework for Cost-Optimised Document Classification
Mohammed Yousif, Prabhjot Singh, Arjun Pankajakshan +1
Real-world document classification pipelines typically apply the same sequence of models to every incoming document, regardless of its complexity or type. This leads to inefficient…
Beyond 'One Language, One Script': Quantifying Orthographic Bias in Multilingual VLMs with PuMVR
Prabhjot Singh, Bhushan Pawar, Madhu Reddiboina
Current Vision-Language Models (VLMs) are celebrated for their multilingual capabilities, yet they operate under a flawed assumption: that one language corresponds to a single writ…
Not Truly Multilingual: Script Consistency as a Missing Dimension in VLM Evaluation
Prabhjot Singh, Bhushan Pawar, Madhu Reddiboina +1
Current multilingual evaluations for Vision-Language Models (VLMs) assume a one-to-one mapping between language and orthography, overlooking billions of users of multi-script langu…
Enhancing Generalization in Audio Deepfake Detection: A Neural Collapse based Sampling and Training Approach
Mohammed Yousif, Jonat John Mathew, Huzaifa Pallan +5
Generalization in audio deepfake detection presents a significant challenge, with models trained on specific datasets often struggling to detect deepfakes generated under varying c…
Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms
Jonat John Mathew, Rakin Ahsan, Sae Furukawa +6
Deepfake audio poses a rising threat in communication platforms, necessitating real-time detection for audio stream integrity. Unlike traditional non-real-time approaches, this stu…
All Data Inclusive, Deep Learning Models to Predict Critical Events in the Medical Information Mart for Intensive Care III Database (MIMIC III)
Anubhav Reddy Nallabasannagari, Madhu Reddiboina, Ryan Seltzer +3
Intensive care clinicians need reliable clinical practice tools to preempt unexpected critical events that might harm their patients in intensive care units (ICU), to pre-plan time…