Zobrazeno 1 - 10 of 13 pro vyhledávání: '"Nilaksh Das"'
1
A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling
Autor: Renzhi Wu, Nilaksh Das, Sanya Chaba, Sakshi Gandhi, Duen Horng Chau, Xu Chu
Publikováno v: Journal of Data and Information Quality. 14:1-23
Few-shot learning (FSL) aims at learning to generalize from only a small number of labeled examples for a given target task. Most current state-of-the-art FSL methods typically have two limitations. First, they usually require access to a source data
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b9212a50c5417c9ec9c74c946d2a896f
https://doi.org/10.1145/3491232
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6
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning
Autor: Duen Horng Chau, Monica Sunkara, Sundararajan Srinivasan, Sravan Bodapati, Nilaksh Das
Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech. This becomes a bottleneck for training robust models for accented speech which typically contains high variability in pronunciation and
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6deedde2adb061438fdebc760cb880fb
http://arxiv.org/abs/2103.05834
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7
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Autor: Zijie J. Wang, Nilaksh Das, Haekyu Park, Omar Shaikh, Minsuk Kahng, Fred Hohman, Robert Turko, Duen Horng Polo Chau
Publikováno v: IEEE transactions on visualization and computer graphics. 27(2)
Deep learning's great success motivates many practitioners and students to learn about this exciting technology. However, it is often challenging for beginners to take their first step due to the complexity of understanding and applying deep learning
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c022c43a4547d75744c5fd5e3f35b391
https://pubmed.ncbi.nlm.nih.gov/33048723
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8
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks
Autor: Zijie J. Wang, Duen Horng Chau, Nilaksh Das, Haekyu Park, Fred Hohman, Robert Firstman, Emily Rogers
Publikováno v: IEEE VIS (Short Papers)
Deep neural networks (DNNs) are now commonly used in many domains. However, they are vulnerable to adversarial attacks: carefully crafted perturbations on data inputs that can fool a model into making incorrect predictions. Despite significant resear
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9eca2a1f923ad71e946148dc16d4f6e
https://doi.org/10.1109/vis47514.2020.00061
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9
GOGGLES: Automatic Image Labeling with Affinity Coding
Autor: Xu Chu, Sakshi Gandhi, Renzhi Wu, Sanya Chaba, Nilaksh Das, Duen Horng Chau
Publikováno v: SIGMOD Conference
Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has been proposed to reduce the human cost in labeling training data. Howe
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6b0076c614c5c28028e69fb87bd5086
https://doi.org/10.1145/3318464.3380592
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10
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning
Autor: Duen Horng Chau, Zijie J. Wang, Robert Firstman, Haekyu Park, Emily Rogers, Nilaksh Das, Fred Hohman
Publikováno v: CHI Extended Abstracts
Deep neural networks (DNNs) are increasingly powering high-stakes applications such as autonomous cars and healthcare; however, DNNs are often treated as "black boxes" in such applications. Recent research has also revealed that DNNs are highly vulne
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39f21e879eb5699156179d01493a744c
https://doi.org/10.1145/3334480.3382977
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