Zobrazeno 1 - 10
of 9 945
pro vyhledávání: '"ANMOL ."'
Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both. This paper introduces a physics-informed machine learning-based framework to enhance grid's resil
Externí odkaz:
http://arxiv.org/abs/2411.18050
Reservoir computing (RC), is a class of computational methods such as Echo State Networks (ESN) and Liquid State Machines (LSM) describe a generic method to perform pattern recognition and temporal analysis with any non-linear system. This is enabled
Externí odkaz:
http://arxiv.org/abs/2411.11414
The construction industry faces high risks due to frequent accidents, often leaving workers in perilous situations where rapid response is critical. Traditional safety monitoring methods, including wearable sensors and GPS, often fail under obstructi
Externí odkaz:
http://arxiv.org/abs/2411.03016
AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification
Autor:
Hogan, Brendan, Kabra, Anmol, Pacheco, Felipe Siqueira, Greenstreet, Laura, Fan, Joshua, Ferber, Aaron, Ummus, Marta, Brito, Alecsander, Graham, Olivia, Aoki, Lillian, Harvell, Drew, Flecker, Alex, Gomes, Carla
Trust and interpretability are crucial for the use of Artificial Intelligence (AI) in scientific research, but current models often operate as black boxes offering limited transparency and justifications for their outputs. We introduce AiSciVision, a
Externí odkaz:
http://arxiv.org/abs/2410.21480
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This
Externí odkaz:
http://arxiv.org/abs/2410.19179
Large Language Models (LLMs) are increasingly used to assess NLP tasks due to their ability to generate human-like judgments. Single LLMs were used initially, however, recent work suggests using multiple LLMs as judges yields improved performance. An
Externí odkaz:
http://arxiv.org/abs/2410.10215
The nuclear ground state properties of 67 80As nuclei have been investigated within the framework of relativistic mean field (RMF) approach. The RMF model with density dependent (DDME2) interaction is utilized for the calculation of potential energy
Externí odkaz:
http://arxiv.org/abs/2410.07578
We present a framework for designing scores to summarize performance metrics. Our design has two multi-criteria objectives: (1) improving on scores should improve all performance metrics, and (2) achieving pareto-optimal scores should achieve pareto-
Externí odkaz:
http://arxiv.org/abs/2410.06290
Autor:
Mekala, Anmol, Dorna, Vineeth, Dubey, Shreya, Lalwani, Abhishek, Koleczek, David, Rungta, Mukund, Hasan, Sadid, Lobo, Elita
Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model. However, existing unlearning methods for Large Language Models (LLMs) face a critical challenge: they rely solely on ne
Externí odkaz:
http://arxiv.org/abs/2409.13474
Autor:
He, Zecheng, Sun, Bo, Juefei-Xu, Felix, Ma, Haoyu, Ramchandani, Ankit, Cheung, Vincent, Shah, Siddharth, Kalia, Anmol, Subramanyam, Harihar, Zareian, Alireza, Chen, Li, Jain, Ankit, Zhang, Ning, Zhang, Peizhao, Sumbaly, Roshan, Vajda, Peter, Sinha, Animesh
Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. Unlike conventional tuning-based persona
Externí odkaz:
http://arxiv.org/abs/2409.13346