Search Results for author: Duen Horng Chau

Found 41 papers, 22 papers with code

A Search Engine for Discovery of Scientific Challenges and Directions

1 code implementation31 Aug 2021 Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld, Tom Hope

To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.

NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks

1 code implementation29 Aug 2021 Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P. Wright, Omar Shaikh, Fred Hohman, Duen Horng Chau

Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts.

Semantic Similarity Semantic Textual Similarity

Quantifying the Impact of Human Capital, Job History, and Language Factors on Job Seniority with a Large-scale Analysis of Resumes

no code implementations15 Jun 2021 Austin P Wright, Caleb Ziems, Haekyu Park, Jon Saad-Falcon, Duen Horng Chau, Diyi Yang, Maria Tomprou

As job markets worldwide have become more competitive and applicant selection criteria have become more opaque, and different (and sometimes contradictory) information and advice is available for job seekers wishing to progress in their careers, it has never been more difficult to determine which factors in a r\'esum\'e most effectively help career progression.

EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models

2 code implementations30 Mar 2021 Omar Shaikh, Jon Saad-Falcon, Austin P Wright, Nilaksh Das, Scott Freitas, Omar Isaac Asensio, Duen Horng Chau

The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language.

Dodrio: Exploring Transformer Models with Interactive Visualization

1 code implementation ACL 2021 Zijie J. Wang, Robert Turko, Duen Horng Chau

Why do large pre-trained transformer-based models perform so well across a wide variety of NLP tasks?

RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization

no code implementations8 Feb 2021 Austin P Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Duen Horng Chau, Diyi Yang

With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments.

MalNet: A Large-Scale Cybersecurity Image Database of Malicious Software

1 code implementation31 Jan 2021 Scott Freitas, Rahul Duggal, Duen Horng Chau

Computer vision is playing an increasingly important role in automated malware detection with to the rise of the image-based binary representation.

Feature Engineering Malware Detection

SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models

no code implementations26 Jan 2021 Haekyu Park, Zijie J. Wang, Nilaksh Das, Anindya S. Paul, Pruthvi Perumalla, Zhiyan Zhou, Duen Horng Chau

Skeleton-based human action recognition technologies are increasingly used in video based applications, such as home robotics, healthcare on aging population, and surveillance.

Action Recognition

A Large-Scale Database for Graph Representation Learning

2 code implementations16 Nov 2020 Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng Chau

The unprecedented scale and diversity of MalNet offers exciting opportunities to advance the frontiers of graph representation learning---enabling new discoveries and research into imbalanced classification, explainability and the impact of class hardness.

Graph Representation Learning imbalanced classification

A Comparative Analysis of Industry Human-AI Interaction Guidelines

no code implementations22 Oct 2020 Austin P. Wright, Zijie J. Wang, Haekyu Park, Grace Guo, Fabian Sperrle, Mennatallah El-Assady, Alex Endert, Daniel Keim, Duen Horng Chau

We have then used this framework to compare each of the surveyed companies to find differences in areas of emphasis.

Human-Computer Interaction

Examining the Ordering of Rhetorical Strategies in Persuasive Requests

1 code implementation Findings of the Association for Computational Linguistics 2020 Omar Shaikh, Jiaao Chen, Jon Saad-Falcon, Duen Horng Chau, Diyi Yang

We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.

Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations

no code implementations14 Sep 2020 Fabian Sperrle, Mennatallah El-Assady, Grace Guo, Duen Horng Chau, Alex Endert, Daniel Keim

This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches.

Explainable artificial intelligence

Mapping Researchers with PeopleMap

1 code implementation31 Aug 2020 Jon Saad-Falcon, Omar Shaikh, Zijie J. Wang, Austin P. Wright, Sasha Richardson, Duen Horng Chau

Discovering research expertise at universities can be a difficult task.

ELF: An Early-Exiting Framework for Long-Tailed Classification

no code implementations22 Jun 2020 Rahul Duggal, Scott Freitas, Sunny Dhamnani, Duen Horng Chau, Jimeng Sun

The natural world often follows a long-tailed data distribution where only a few classes account for most of the examples.

Classification General Classification

Evaluating Graph Vulnerability and Robustness using TIGER

1 code implementation10 Jun 2020 Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, Duen Horng Chau

By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field.

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

5 code implementations30 Apr 2020 Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau

Deep learning's great success motivates many practitioners and students to learn about this exciting technology.

UnMask: Adversarial Detection and Defense Through Robust Feature Alignment

2 code implementations21 Feb 2020 Scott Freitas, Shang-Tse Chen, Zijie J. Wang, Duen Horng Chau

UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its robust features.

Medical Diagnosis Self-Driving Cars

REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

1 code implementation29 Jan 2020 Rahul Duggal, Scott Freitas, Cao Xiao, Duen Horng Chau, Jimeng Sun

By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference.

EEG Neural Network Compression +1

Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning

no code implementations21 Jan 2020 Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau

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.

Adversarial Attack

RECAST: Interactive Auditing of Automatic Toxicity Detection Models

no code implementations7 Jan 2020 Austin P. Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Diyi Yang, Duen Horng Chau

As toxic language becomes nearly pervasive online, there has been increasing interest in leveraging the advancements in natural language processing (NLP), from very large transformer models to automatically detecting and removing toxic comments.


CNN 101: Interactive Visual Learning for Convolutional Neural Networks

no code implementations7 Jan 2020 Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau

The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology.

ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features

no code implementations20 Aug 2019 Xiangyun Lei, Fred Hohman, Duen Horng Chau, Andrew J. Medford

In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory.

NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions

no code implementations2 Jun 2019 Haekyu Park, Fred Hohman, Duen Horng Chau

As deep neural networks are increasingly used in solving high-stake problems, there is a pressing need to understand their internal decision mechanisms.

Talk Proposal: Towards the Realistic Evaluation of Evasion Attacks using CARLA

3 code implementations18 Apr 2019 Cory Cornelius, Shang-Tse Chen, Jason Martin, Duen Horng Chau

In this talk we describe our content-preserving attack on object detectors, ShapeShifter, and demonstrate how to evaluate this threat in realistic scenarios.

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

1 code implementation10 Apr 2019 Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau

We present FairVis, a mixed-initiative visual analytics system that integrates a novel subgroup discovery technique for users to audit the fairness of machine learning models.


GOGGLES: Automatic Image Labeling with Affinity Coding

1 code implementation11 Mar 2019 Nilaksh Das, Sanya Chaba, Renzhi Wu, Sakshi Gandhi, Duen Horng Chau, Xu Chu

We build the GOGGLES system that implements affinity coding for labeling image datasets by designing a novel set of reusable affinity functions for images, and propose a novel hierarchical generative model for class inference using a small development set.

Few-Shot Learning

The Efficacy of SHIELD under Different Threat Models

no code implementations1 Feb 2019 Cory Cornelius, Nilaksh Das, Shang-Tse Chen, Li Chen, Michael E. Kounavis, Duen Horng Chau

To evaluate the robustness of the defense against an adaptive attacker, we consider the targeted-attack success rate of the Projected Gradient Descent (PGD) attack, which is a strong gradient-based adversarial attack proposed in adversarial machine learning research.

Adversarial Attack Image Classification

GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation

1 code implementation5 Sep 2018 Minsuk Kahng, Nikhil Thorat, Duen Horng Chau, Fernanda Viégas, Martin Wattenberg

Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology.

Interactive Classification for Deep Learning Interpretation

1 code implementation14 Jun 2018 Ángel Alexander Cabrera, Fred Hohman, Jason Lin, Duen Horng Chau

We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers.

Classification General Classification

ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio

no code implementations30 May 2018 Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Li Chen, Michael E. Kounavis, Duen Horng Chau

Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible perturbations to audio samples.

Adversarial Attack automatic-speech-recognition +1

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

3 code implementations16 Apr 2018 Shang-Tse Chen, Cory Cornelius, Jason Martin, Duen Horng Chau

Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work.

Adversarial Attack Autonomous Vehicles +3

Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

3 code implementations19 Feb 2018 Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau

The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images.

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

no code implementations21 Jan 2018 Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau

We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where).

Decision Making

Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression

no code implementations8 May 2017 Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E. Kounavis, Duen Horng Chau

Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition.

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

no code implementations6 Apr 2017 Minsuk Kahng, Pierre Y. Andrews, Aditya Kalro, Duen Horng Chau

While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge.

M3: Scaling Up Machine Learning via Memory Mapping

no code implementations11 Apr 2016 Dezhi Fang, Duen Horng Chau

To process data that do not fit in RAM, conventional wisdom would suggest using distributed approaches.

Graph Mining

Communication Efficient Distributed Agnostic Boosting

no code implementations21 Jun 2015 Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau

We consider the problem of learning from distributed data in the agnostic setting, i. e., in the presence of arbitrary forms of noise.

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