Search Results for author: Fred Hohman

Found 16 papers, 8 papers with code

Symphony: Composing Interactive Interfaces for Machine Learning

no code implementations18 Feb 2022 Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz

Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems.

Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

1 code implementation24 Oct 2021 Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances.

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

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.

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

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.

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.

Fairness

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

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

A Deep Learning Approach for Population Estimation from Satellite Imagery

no code implementations30 Aug 2017 Caleb Robinson, Fred Hohman, Bistra Dilkina

We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs.

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.

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