Výsledky vyhledávání - "Chitta, Radha"

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Answering Questions in Stages: Prompt Chaining for Contract QA

Finding answers to legal questions about clauses in contracts is an important form of analysis in many legal workflows (e.g., understanding market trends, due diligence, risk mitigation) but more important is being able to do this at scale. Prior wor

Externí odkaz: http://arxiv.org/abs/2410.12840

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A Search for Prompts: Generating Structured Answers from Contracts

Autor: Roegiest, Adam, Chitta, Radha, Donnelly, Jonathan, Lash, Maya, Vtyurina, Alexandra, Longtin, François

In many legal processes being able to action on the concrete implication of a legal question can be valuable to automating human review or signalling certain conditions (e.g., alerts around automatic renewal). To support such tasks, we present a form

Externí odkaz: http://arxiv.org/abs/2310.10141

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Long-Term Memory Networks for Question Answering

Autor: Ma, Fenglong, Chitta, Radha, Kataria, Saurabh, Zhou, Jing, Ramesh, Palghat, Sun, Tong, Gao, Jing

Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task. Several deep neural network architectures have been deve

Externí odkaz: http://arxiv.org/abs/1707.01961

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Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Autor: Ma, Fenglong, Chitta, Radha, Zhou, Jing, You, Quanzeng, Sun, Tong, Gao, Jing

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visi

Externí odkaz: http://arxiv.org/abs/1706.05764

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Deep Multimodal Representation Learning from Temporal Data

Autor: Yang, Xitong, Ramesh, Palghat, Chitta, Radha, Madhvanath, Sriganesh, Bernal, Edgar A., Luo, Jiebo

In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video,

Externí odkaz: http://arxiv.org/abs/1704.03152

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Scalable Kernel Clustering: Approximate Kernel k-means

Autor: Chitta, Radha, Jin, Rong, Havens, Timothy C., Jain, Anil K.

Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data. Among various kernel-based clustering algorithms, kernel k-means has gained popularity due to its simple iterative nature and ease of implemen

Externí odkaz: http://arxiv.org/abs/1402.3849

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Two-level k-means clustering algorithm for k-tau relationship establishment and linear-time classification

Autor: Chitta, Radha, Murty, M Narasimha

Publikováno v: IndraStra Global.

Partitional clustering algorithms, which partition the dataset into a pre-defined number of clusters, can be broadly classified into two types: algorithms which explicitly take the number of clusters as input and algorithms that take the expected siz

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::02b91bcd85840f4a03999312a51551b5
https://igi.indrastra.com/items/show/195647