37 citations · 129 across the 31 of their papers we have counts for
4 papers · 1 filter
Uncertainty Quantification in Continual Open-World Learning
Amanda S. Rios, Ibrahima J. Ndiour, Parual Datta +3
AI deployed in the real-world should be capable of autonomously adapting to novelties encountered after deployment. Yet, in the field of continual learning, the reliance on novelty…
CONCLAD: COntinuous Novel CLAss Detector
Amanda Rios, Ibrahima Ndiour, Parual Datta +2
In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD ("COntinuous Novel CLAss Det…
CUAL: Continual Uncertainty-aware Active Learner
Amanda Rios, Ibrahima Ndiour, Parual Datta +3
AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic cont…
Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
Ranganath Krishnan, Piyush Khanna, Omesh Tickoo
Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities. However, these models…