2 citations · 4 across the 25 of their papers we have counts for
5 papers · 1 filter
Detecting AI Hallucinations in Finance: An Information-Theoretic Method Cuts Hallucination Rate by 92%
Mainak Singha
Large language models (LLMs) produce fluent but unsupported answers - hallucinations - limiting safe deployment in high-stakes domains. We propose ECLIPSE, a framework that treats…
How (Mis)calibrated is your Federated CLIP and what to do about it?
Mainak Singha, Masih Aminbeidokhti, Paolo Casari +3
Vision-language models (VLMs) such as CLIP are increasingly adapted across decentralized data silos, yet the reliability of their predictions under federated learning (FL) remains…
Learning Under Laws: A Constraint-Projected Neural PDE Solver that Eliminates Hallucinations
Mainak Singha
Neural networks can approximate solutions to partial differential equations, but they often break the very laws they are meant to model-creating mass from nowhere, drifting shocks,…
FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models
Mainak Singha, Subhankar Roy, Sarthak Mehrotra +4
In federated learning, textual prompt tuning adapts Vision-Language Models (e.g., CLIP) by tuning lightweight input tokens (or prompts) on local client data, while keeping network…
OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP
Mohamad Hassan N C, Divyam Gupta, Mainak Singha +4
We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG). While prompt-based methods usi…