3 citations · 3 across the 20 of their papers we have counts for
11 papers · 1 filter
Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs
Ayush Gupta, Ramneet Kaur, Anirban Roy +3
We propose a novel inference-time out-of-domain (OOD) detection algorithm for specialized large language models (LLMs). Despite achieving state-of-the-art performance on in-domain…
Privacy Preserving In-Context-Learning Framework for Large Language Models
Bishnu Bhusal, Manoj Acharya, Ramneet Kaur +5
Large language models (LLMs) have significantly transformed natural language understanding and generation, but they raise privacy concerns due to potential exposure of sensitive in…
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
Chandra Kanth Nagesh, Sriram Sankaranarayanan, Ramneet Kaur +2
We study the problem of learning neural network models for Ordinary Differential Equations (ODEs) with parametric uncertainties. Such neural network models capture the solution to…
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Colin Samplawski, Adam D. Cobb, Manoj Acharya +2
Despite their widespread use, large language models (LLMs) are known to hallucinate incorrect information and be poorly calibrated. This makes the uncertainty quantification of the…
TOGA: Temporally Grounded Open-Ended Video QA with Weak Supervision
Ayush Gupta, Anirban Roy, Rama Chellappa +3
We address the problem of video question answering (video QA) with temporal grounding in a weakly supervised setup, without any temporal annotations. Given a video and a question,…
Backpropagation-Free Metropolis-Adjusted Langevin Algorithm
Adam D. Cobb, Susmit Jha
Recent work on backpropagation-free learning has shown that it is possible to use forward-mode automatic differentiation (AD) to perform optimization on differentiable models. Forw…
On the Evaluation of Engineering Artificial General Intelligence
Sandeep Neema, Susmit Jha, Adam Nagel +6
We discuss the challenges and propose a framework for evaluating engineering artificial general intelligence (eAGI) agents. We consider eAGI as a specialization of artificial gener…
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
Trilok Padhi, Ramneet Kaur, Adam D. Cobb +7
We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for multi-modal large language models (LLMs). Existing state-of-the-art UQ methods rely on co…
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios
Vivian Lin, Ramneet Kaur, Yahan Yang +6
The safety of learning-enabled cyber-physical systems is compromised by the well-known vulnerabilities of deep neural networks to out-of-distribution (OOD) inputs. Existing literat…
AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models
Colin Samplawski, Adam D. Cobb, Susmit Jha
Computer-aided design (CAD) is a promising application area for emerging artificial intelligence methods. Traditional workflows for cyberphysical systems create detailed digital mo…
TeleLoRA: Teleporting Model-Specific Alignment Across LLMs
Xiao Lin, Manoj Acharya, Anirban Roy +1
Mitigating Trojans in Large Language Models (LLMs) is one of many tasks where alignment data is LLM specific, as different LLMs have different Trojan triggers and trigger behaviors…