Výsledky vyhledávání - "Sharma, Prakhar"

Report

ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling

Autor: Nguyen, Tung, Jewik, Jason, Bansal, Hritik, Sharma, Prakhar, Grover, Aditya

Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation and mitigation efforts. In recent years, there has been a surging inte

Externí odkaz: http://arxiv.org/abs/2307.01909

Zobrazit plný text záznamu

Report

Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment

Autor: Li, Zichao, Sharma, Prakhar, Lu, Xing Han, Cheung, Jackie C. K., Reddy, Siva

Publikováno v: Findings of the Association for Computational Linguistics: ACL (2022) 926-937

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment. In this paper, we ask the question: Can we improve QA systems further \emph{post-}deployment based on user interactions? We focu

Externí odkaz: http://arxiv.org/abs/2204.03025

Zobrazit plný text záznamu

Report

Knowledge-Aware Neural Networks for Medical Forum Question Classification

Autor: Roy, Soumyadeep, Chakraborty, Sudip, Mandal, Aishik, Balde, Gunjan, Sharma, Prakhar, Natarajan, Anandhavelu, Khosla, Megha, Sural, Shamik, Ganguly, Niloy

Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically

Externí odkaz: http://arxiv.org/abs/2109.13141

Zobrazit plný text záznamu

Report

Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders

Autor: Sharma, Prakhar, Porras, Phillip, Cheung, Steven, Carpenter, James, Yegneswaran, Vinod

We present a deep learning based approach to containerized application runtime stability analysis, and an intelligent publishing algorithm that can dynamically adjust the depth of process-level forensics published to a backend incident analysis repos

Externí odkaz: http://arxiv.org/abs/2104.13193

Zobrazit plný text záznamu

Report

Exploring Effects of Random Walk Based Minibatch Selection Policy on Knowledge Graph Completion

Autor: Santra, Bishal, Sharma, Prakhar, Roychowdhury, Sumegh, Goyal, Pawan

In this paper, we have explored the effects of different minibatch sampling techniques in Knowledge Graph Completion. Knowledge Graph Completion (KGC) or Link Prediction is the task of predicting missing facts in a knowledge graph. KGC models are usu

Externí odkaz: http://arxiv.org/abs/2004.05553

Zobrazit plný text záznamu

Report

Entity Personalized Talent Search Models with Tree Interaction Features

Autor: Ozcaglar, Cagri, Geyik, Sahin, Schmitz, Brian, Sharma, Prakhar, Shelkovnykov, Alex, Ma, Yiming, Buchanan, Erik

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models

Externí odkaz: http://arxiv.org/abs/1902.09041

Zobrazit plný text záznamu