Search Results for author: Fred Hohman

Found 21 papers, 10 papers with code

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.

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

2 code implementations15 Jun 2023 Zijie J. Wang, Fred Hohman, Duen Horng Chau

Machine learning models often learn latent embedding representations that capture the domain semantics of their training data.

Navigate

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.

BIG-bench Machine Learning

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.

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

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.

BIG-bench Machine Learning Fairness +1

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

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 Management

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.

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.

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.

BIG-bench Machine Learning

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.

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

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.

BIG-bench Machine Learning

Designing Data: Proactive Data Collection and Iteration for Machine Learning

no code implementations24 Jan 2023 Aspen Hopkins, Fred Hohman, Luca Zappella, Xavier Suau Cuadros, Dominik Moritz

Lack of diversity in data collection has caused significant failures in machine learning (ML) applications.

Density Estimation

Collaborative Machine Learning Model Building with Families Using Co-ML

no code implementations11 Apr 2023 Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery, Fred Hohman, Adriana Hilliard, R. Benjamin Shapiro

We share the Co-ML system design and contribute a discussion of how using Co-ML in a collaborative activity enabled beginners to collectively engage with dataset design considerations underrepresented in prior work such as data diversity, class imbalance, and data quality.

Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences

no code implementations6 Oct 2023 Fred Hohman, Mary Beth Kery, Donghao Ren, Dominik Moritz

On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday personal devices.

Model Compression

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