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15 results on '"Narang, Neeru"'

1. Harnessing Business and Media Insights with Large Language Models

Author

Bao, Yujia, Shah, Ankit Parag, Narang, Neeru, Rivers, Jonathan, Maksey, Rajeev, Guan, Lan, Barrere, Louise N., Evenson, Shelley, Basole, Rahul, Miao, Connie, Mehta, Ankit, Boulay, Fabien, Park, Su Min, Pearson, Natalie E., Joy, Eldhose, He, Tiger, Thakur, Sumiran, Ghosal, Koustav, On, Josh, Morrison, Phoebe, Major, Tim, Wang, Eva Siqi, Escobar, Gina, Wei, Jiaheng, Weerasooriya, Tharindu Cyril, Song, Queena, Lashkevich, Daria, Chen, Clare, Kim, Gyuhak, Yin, Dengpan, Hejna, Don, Nomeli, Mo, and Wei, Wei

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performance metrics, and expert insights. Unlike generic LLMs, FALM leverages a curated knowledge base built from professional journalism, enabling it to deliver precise and in-depth answers to intricate business questions. Users can further leverage natural language queries to directly visualize financial data, generating insightful charts and graphs to understand trends across diverse business sectors clearly. FALM fosters user trust and ensures output accuracy through three novel methods: 1) Time-aware reasoning guarantees accurate event registration and prioritizes recent updates. 2) Thematic trend analysis explicitly examines topic evolution over time, providing insights into emerging business landscapes. 3) Content referencing and task decomposition enhance answer fidelity and data visualization accuracy. We conduct both automated and human evaluations, demonstrating FALM's significant performance improvements over baseline methods while prioritizing responsible AI practices. These benchmarks establish FALM as a cutting-edge LLM in the business and media domains, with exceptional accuracy and trustworthiness.

Published
2024

2. Classification of Soft Biometric Traits When Matching Near-Infrared Long-Range Face Images Against Their Visible Counterparts

Author

Narang, Neeru, Bourlai, Thirimachos, Masys, Anthony J., Series Editor, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, and Patel, Vishal M., editor

Published
2020
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3. Deep Feature Learning for Classification When Using Single Sensor Multi-wavelength Based Facial Recognition Systems in SWIR Band

Author

Narang, Neeru, Bourlai, Thirimachos, Masys, Anthony J., Editor-in-chief, Bichler, Gisela, Series editor, Bourlai, Thirimachos, Series editor, Johnson, Chris, Series editor, Karampelas, Panagiotis, Series editor, Leuprecht, Christian, Series editor, Morse, Edward C., Series editor, Skillicorn, David, Series editor, and Yamagata, Yoshiki, Series editor

Published
2018
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15. Deep Features based Hierarchical Classification Scheme for Face Recognition in Heterogeneous Environments

Author

Narang, Neeru

Abstract

This dissertation investigates the advantages and limitations of the heterogeneous problem of matching infrared long-range, night time face images against their visible counterparts. The contributions of the thesis are three-fold. First, a multifeature scenario dependent fusion scheme is developed, where Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Next, a set of fusion score level schemes are developed and applied before face matching. The developed fusion scheme results in 54 percent point (pp) higher recognition performance than the baseline established using a commercial and a set of academic face matchers. Second, to further improve the performance of baseline face recognition (FR) systems, a scenario dependent and sensor adaptable convolutional neural network (CNN) is developed that groups the data in terms of demographic information, including scenarios (situations) such as indoors or outdoors data, as well as distance and sensor based data. The automated grouping scheme developed is applied before the FR algorithms are used, improving baseline performance from 48% (all vs. all) to 70% (with data grouping). Third, an image quality restoration scheme is designed and developed. This scheme is beneficial to FR systems because the quality of face data captured under challenging conditions is affected by a variety of noise factors (including low illumination conditions, variable standoff distances, and uncooperative subjects). Thus, image quality is responsible for the performance degradation of conventional FR matchers. The developed scheme improves, first, the quality of distorted face images and, then, FR performance in terms of the rank-1 identification rate. Based on the experimental results the major conclusion from this research is that the aforementioned schemes discussed in this dissertation significantly improve cross-spectral face matching performance on diverse scenarios, when used either independently or in combination. The experimental results are further supported by statistical analysis tests, conducted to find the statistical significance of incorporating the developed score level fusion schemes, as well as the demographic filtering to FR systems.

Published
2017

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