no code implementations • 10 Apr 2024 • Ruijia Cheng, Titus Barik, Alan Leung, Fred Hohman, Jeffrey Nichols
We present this workflow in BISCUIT, an extension for JupyterLab that provides users with ephemeral UIs generated by LLMs based on the context of their code and intentions, scaffolding users to understand, guide, and explore with LLM-generated code.
no code implementations • 3 Apr 2024 • Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences.
no code implementations • 6 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.
4 code implementations • 15 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.
1 code implementation • 12 Apr 2023 • Samantha Robertson, Zijie J. Wang, Dominik Moritz, Mary Beth Kery, Fred Hohman
Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal.
no code implementations • 11 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.
no code implementations • 24 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.
no code implementations • 18 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.
1 code implementation • 24 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.
1 code implementation • 29 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.
1 code implementation • 5 Sep 2020 • Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau
Deep neural networks (DNNs) are now commonly used in many domains.
5 code implementations • 30 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.
no code implementations • 21 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.
no code implementations • 7 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.
no code implementations • 20 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.
no code implementations • 2 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.
1 code implementation • 10 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.
3 code implementations • 4 Apr 2019 • Fred Hohman, Haekyu Park, Caleb Robinson, Duen Horng Chau
Deep learning is increasingly used in decision-making tasks.
1 code implementation • 14 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.
3 code implementations • 19 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.
no code implementations • 21 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).
no code implementations • 30 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.
no code implementations • 8 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.