Fred Hohman

According to our database, Fred Hohman authored at least 41 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Exploring Empty Spaces: Human-in-the-Loop Data Augmentation.

Catherine Yeh, Donghao Ren, Yannick Assogba, Dominik Moritz, Fred Hohman

CoRR, 2024

AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking.

Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery

CoRR, 2024

Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments.

Angie Boggust, Venkatesh Sivaraman, Yannick Assogba, Donghao Ren, Dominik Moritz, Fred Hohman

CoRR, 2024

Apple Intelligence Foundation Language Models.

Tom Gunter, Zirui Wang, Chong Wang, Ruoming Pang, Andy Narayanan, Aonan Zhang, Bowen Zhang, Chen Chen, Chung-Cheng Chiu, David Qiu, Deepak Gopinath, Dian Ang Yap, Dong Yin, Feng Nan, Floris Weers, Guoli Yin, Haoshuo Huang, Jianyu Wang, Jiarui Lu, John Peebles, Ke Ye, Mark Lee, Nan Du, Qibin Chen, Quentin Keunebroek, Sam Wiseman, Syd Evans, Tao Lei, Vivek Rathod, Xiang Kong, Xianzhi Du, Yanghao Li, Yongqiang Wang, Yuan Gao, Zaid Ahmed, Zhaoyang Xu, Zhiyun Lu, Al Rashid, Albin Madappally Jose, Alec Doane, Alfredo Bencomo, Allison Vanderby, Andrew Hansen, Ankur Jain, Anupama Mann Anupama, Areeba Kamal, Bugu Wu, Carolina Brum, Charlie Maalouf, Chinguun Erdenebileg, Chris Dulhanty, Dominik Moritz, Doug Kang, Eduardo Jimenez, Evan Ladd, Fangping Shi, Felix Bai, Frank Chu, Fred Hohman, Hadas Kotek, Hannah Gillis Coleman, Jane Li, Jeffrey P. Bigham, Jeffery Cao, Jeff Lai, Jessica Cheung, Jiulong Shan, Joe Zhou, John Li, Jun Qin, Karanjeet Singh, Karla Vega, Kelvin Zou, Laura Heckman, Lauren Gardiner, Margit Bowler, Maria Cordell, Meng Cao, Nicole Hay, Nilesh Shahdadpuri, Otto Godwin, Pranay Dighe, Pushyami Rachapudi, Ramsey Tantawi, Roman Frigg, Sam Davarnia, Sanskruti Shah, Saptarshi Guha, Sasha Sirovica, Shen Ma, Shuang Ma, Simon Wang, Sulgi Kim, Suma Jayaram, Vaishaal Shankar, Varsha Paidi, Vivek Kumar, Xin Wang, Xin Zheng, Walker Cheng

CoRR, 2024

BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks.

Ruijia Cheng, Titus Barik, Alan Leung, Fred Hohman, Jeffrey Nichols

Proceedings of the 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2024

Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference.

Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang

Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

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

Fred Hohman, Mary Beth Kery, Donghao Ren, Dominik Moritz

Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

2023

Designing Data: Proactive Data Collection and Iteration for Machine Learning.

Aspen K. Hopkins, Fred Hohman, Luca Zappella, Xavier Suau Cuadros, Dominik Moritz

CoRR, 2023

Angler: Helping Machine Translation Practitioners Prioritize Model Improvements.

Samantha Robertson, Zijie J. Wang, Dominik Moritz, Mary Beth Kery, Fred Hohman

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

Collaborative Machine Learning Model Building with Families Using Co-ML.

Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery, Fred Hohman, Adriana Hilliard, R. Benjamin Shapiro

Proceedings of the 22nd Annual ACM Interaction Design and Children Conference, 2023

WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings.

Zijie J. Wang, Fred Hohman, Duen Horng Chau

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2023

2022

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

Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P. Wright, Omar Shaikh, Fred Hohman, Duen Horng (Polo) Chau

IEEE Trans. Vis. Comput. Graph., 2022

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

Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel

Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

Symphony: Composing Interactive Interfaces for Machine Learning.

Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz

Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

2021

Interactive Scalable Interfaces for Machine Learning Interpretability.

Frederick Hohman

PhD thesis, 2021

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization.

Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng (Polo) Chau

IEEE Trans. Vis. Comput. Graph., 2021

2020

Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations.

Fred Hohman, Haekyu Park, Caleb Robinson, Duen Horng (Polo) Chau

IEEE Trans. Vis. Comput. Graph., 2020

Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks.

Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng (Polo) Chau

Proceedings of the 31st IEEE Visualization Conference, 2020

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks.

Mary Beth Kery, Donghao Ren, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Kayur Patel

Proceedings of the UIST '20: The 33rd Annual ACM Symposium on User Interface Software and Technology, 2020

CNN 101: Interactive Visual Learning for Convolutional Neural Networks.

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

Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020

The Future of Notebook Programming Is Fluid.

Mary Beth Kery, Donghao Ren, Kanit Wongsuphasawat, Fred Hohman, Kayur Patel

Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020

Understanding and Visualizing Data Iteration in Machine Learning.

Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, Kayur Patel

Proceedings of the CHI '20: CHI Conference on Human Factors in Computing Systems, 2020

Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning.

Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau

Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020

2019

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

Fred Hohman, Minsuk Kahng, Robert S. Pienta, Duen Horng Chau

IEEE Trans. Vis. Comput. Graph., 2019

NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions.

Haekyu Park, Fred Hohman, Duen Horng Chau

CoRR, 2019

ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features.

Xiangyun Lei, Fred Hohman, Duen Horng (Polo) Chau, Andrew J. Medford

Proceedings of the 30th IEEE Visualization Conference, 2019

TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning.

Fred Hohman, Arjun Srinivasan, Steven Mark Drucker

Proceedings of the 30th IEEE Visualization Conference, 2019

Atlas: local graph exploration in a global context.

James Abello, Fred Hohman, Varun Bezzam, Duen Horng Chau

Proceedings of the 24th International Conference on Intelligent User Interfaces, 2019

FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning.

Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau

Proceedings of the 14th IEEE Conference on Visual Analytics Science and Technology, 2019

Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models.

Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, Steven Mark Drucker

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

Managing Messes in Computational Notebooks.

Andrew Head, Fred Hohman, Titus Barik, Steven Mark Drucker, Robert DeLine

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

2018

VIGOR: Interactive Visual Exploration of Graph Query Results.

Robert S. Pienta, Fred Hohman, Alex Endert, Acar Tamersoy, Kevin A. Roundy, Christopher S. Gates, Shamkant B. Navathe, Duen Horng Chau

IEEE Trans. Vis. Comput. Graph., 2018

Large Graph Exploration via Subgraph Discovery and Decomposition.

James Abello, Fred Hohman, Varun Bezzam, Duen Horng Chau

CoRR, 2018

Interactive Classification for Deep Learning Interpretation.

Ángel Alexander Cabrera, Fred Hohman, Jason Lin, Duen Horng Chau

CoRR, 2018

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

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

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Scalable K-Core Decomposition for Static Graphs Using a Dynamic Graph Data Structure.

Alok Tripathy, Fred Hohman, Duen Horng Chau, Oded Green

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

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

Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E. Kounavis, Duen Horng Chau

CoRR, 2017

Visual Graph Query Construction and Refinement.

Robert S. Pienta, Fred Hohman, Acar Tamersoy, Alex Endert, Shamkant B. Navathe, Hanghang Tong, Duen Horng Chau

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

mHealth visual discovery dashboard.

Dezhi Fang, Fred Hohman, Peter J. Polack Jr., Hillol Sarker, Minsuk Kahng, Moushumi Sharmin, Mustafa al'Absi, Duen Horng Chau

Proceedings of the Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, 2017

A Deep Learning Approach for Population Estimation from Satellite Imagery.

Caleb Robinson, Fred Hohman, Bistra Dilkina

Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, 2017

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.

Fred Hohman, Nathan O. Hodas, Duen Horng Chau

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017