9 citations · 15 across the 6 of their papers we have counts for
6 papers
Transparent AI: The Case for Interpretability and Explainability
Dhanesh Ramachandram, Himanshu Joshi, Judy Zhu +3
As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation.…
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation
Ananya Raval, Aravind Narayanan, Vahid Reza Khazaie +1
Large Multimodal Models (LMMs) are typically trained on vast corpora of image-text data but are often limited in linguistic coverage, leading to biased and unfair outputs across la…
CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models
Dhanesh Ramachandram, Ananya Raval
Competing risks are crucial considerations in survival modelling, particularly in healthcare domains where patients may experience multiple distinct event types. We propose CRISP-N…
A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice
Shaina Raza, Mizanur Rahman, Safiullah Kamawal +4
Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions. This survey reviews the progress in RS inclusively from 201…
MBIAS: Mitigating Bias in Large Language Models While Retaining Context
Shaina Raza, Ananya Raval, Veronica Chatrath
The deployment of Large Language Models (LLMs) in diverse applications necessitates an assurance of safety without compromising the contextual integrity of the generated content. T…
Dynamic Load Balancing Strategies for Graph Applications on GPUs
Ananya Raval, Rupesh Nasre, Vivek Kumar +3
Acceleration of graph applications on GPUs has found large interest due to the ubiquitous use of graph processing in various domains. The inherent \textit{irregularity} in graph ap…