Search Results for author:
Found 14 papers, 3 papers with code
ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science
We train a graph neural network on the curated document graph to act as a structural encoder for the corresponding passages retrieved during the model pretraining.
Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science
This research investigates how uncertainty scores vary when scientific knowledge is incorporated as pretraining and retrieval data and explores the relationship between uncertainty scores and the accuracy of model-generated outputs.
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations.
NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain
As LLMs have become increasingly popular, they have been used in almost every field.
SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions
Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent.
A General Framework for Uncertainty Quantification via Neural SDE-RNN
Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements.
There is more to graphs than meets the eye: Learning universal features with self-supervision
We hypothesize that leveraging multiple graphs of the same type/class can improve the quality of learnt representations in the model by extracting features that are universal to the class of graphs.
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids
Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness.
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization
However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network.
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data.
An Information Theoretic approach to identify Dominant Voltage Influencers for Unbalanced Distribution Systems
VIS is derived analytically in a computationally efficient manner and its efficacy to identify DVI nodes is validated using the IEEE 37-node test system.
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks
Most existing methods of critical node identification are based on an iterative approach that explores each node/link of a graph.
Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks
To overcome these challenges, this article proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes/links in large complex networks.
