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Fred Hohman
Frederick Hohman
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- affiliation: Georgia Institute of Technology, Atlanta, GA, USA
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Books and Theses
- 2021
- [b1]Frederick Hohman:
Interactive Scalable Interfaces for Machine Learning Interpretability. Georgia Institute of Technology, Atlanta, GA, USA, 2021
Journal Articles
- 2025
- [j6]Angie Boggust, Venkatesh Sivaraman, Yannick Assogba, Donghao Ren, Dominik Moritz, Fred Hohman:
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments. IEEE Trans. Vis. Comput. Graph. 31(1): 809-819 (2025) - 2022
- [j5]Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P. Wright, Omar Shaikh, Fred Hohman, Duen Horng (Polo) Chau:
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks. IEEE Trans. Vis. Comput. Graph. 28(1): 813-823 (2022) - 2021
- [j4]Zijie J. Wang
, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng (Polo) Chau
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Trans. Vis. Comput. Graph. 27(2): 1396-1406 (2021) - 2020
- [j3]Fred Hohman, Haekyu Park, Caleb Robinson, Duen Horng (Polo) Chau:
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations. IEEE Trans. Vis. Comput. Graph. 26(1): 1096-1106 (2020) - 2019
- [j2]Fred Hohman
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers. IEEE Trans. Vis. Comput. Graph. 25(8): 2674-2693 (2019) - 2018
- [j1]Robert S. Pienta, Fred Hohman, Alex Endert, Acar Tamersoy, Kevin A. Roundy
VIGOR: Interactive Visual Exploration of Graph Query Results. IEEE Trans. Vis. Comput. Graph. 24(1): 215-225 (2018)
Conference and Workshop Papers
- 2024
- [c26]Fred Hohman
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences. CHI 2024: 645:1-645:18 - [c25]Fred Hohman
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. CHI 2024: 648:1-648:19 - [c24]Ruijia Cheng, Titus Barik, Alan Leung, Fred Hohman, Jeffrey Nichols:
BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks. VL/HCC 2024: 13-23 - 2023
- [c23]Zijie J. Wang, Fred Hohman, Duen Horng Chau:
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings. ACL (demo) 2023: 516-523 - [c22]Tiffany Tseng
Collaborative Machine Learning Model Building with Families Using Co-ML. IDC 2023: 40-51 - [c21]Samantha Robertson
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements. CHI 2023: 832:1-832:20 - 2022
- [c20]Alex Bäuerle, Ángel Alexander Cabrera
Symphony: Composing Interactive Interfaces for Machine Learning. CHI 2022: 210:1-210:14 - [c19]Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel:
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. CHI 2022: 408:1-408:13 - 2020
- [c18]Nilaksh Das, Haekyu Park, Zijie J. Wang
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning. CHI Extended Abstracts 2020: 1-7 - [c17]Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, Kayur Patel:
Understanding and Visualizing Data Iteration in Machine Learning. CHI 2020: 1-13 - [c16]Mary Beth Kery, Donghao Ren, Kanit Wongsuphasawat, Fred Hohman, Kayur Patel:
The Future of Notebook Programming Is Fluid. CHI Extended Abstracts 2020: 1-8 - [c15]Zijie J. Wang
CNN 101: Interactive Visual Learning for Convolutional Neural Networks. CHI Extended Abstracts 2020: 1-7 - [c14]Mary Beth Kery, Donghao Ren, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Kayur Patel:
mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks. UIST 2020: 140-151 - [c13]Nilaksh Das, Haekyu Park, Zijie J. Wang
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks. IEEE VIS (Short Papers) 2020: 271-275 - 2019
- [c12]Andrew Head
Managing Messes in Computational Notebooks. CHI 2019: 270 - [c11]Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, Steven Mark Drucker:
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. CHI 2019: 579 - [c10]Ángel Alexander Cabrera
FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. VAST 2019: 46-56 - [c9]James Abello, Fred Hohman, Varun Bezzam, Duen Horng Chau
Atlas: local graph exploration in a global context. IUI 2019: 165-176 - [c8]Fred Hohman, Arjun Srinivasan, Steven Mark Drucker:
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning. IEEE VIS (Short Papers) 2019: 151-155 - [c7]Xiangyun Lei
ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features. IEEE VIS (Short Papers) 2019: 196-200 - 2018
- [c6]Alok Tripathy, Fred Hohman, Duen Horng Chau
Scalable K-Core Decomposition for Static Graphs Using a Dynamic Graph Data Structure. IEEE BigData 2018: 1134-1141 - [c5]Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau
SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression. KDD 2018: 196-204 - 2017
- [c4]Fred Hohman, Nathan O. Hodas, Duen Horng Chau
ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation. CHI Extended Abstracts 2017: 1694-1699 - [c3]Caleb Robinson, Fred Hohman, Bistra Dilkina
A Deep Learning Approach for Population Estimation from Satellite Imagery. GeoHumanities@SIGSPATIAL 2017: 47-54 - [c2]Dezhi Fang, Fred Hohman, Peter J. Polack Jr., Hillol Sarker, Minsuk Kahng, Moushumi Sharmin, Mustafa al'Absi, Duen Horng Chau
mHealth visual discovery dashboard. UbiComp/ISWC Adjunct 2017: 237-240 - [c1]Robert S. Pienta, Fred Hohman, Acar Tamersoy, Alex Endert, Shamkant B. Navathe, Hanghang Tong
Visual Graph Query Construction and Refinement. SIGMOD Conference 2017: 1587-1590
Informal and Other Publications
- 2024
- [i29]Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang:
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. CoRR abs/2404.03085 (2024) - [i28]Ruijia Cheng, Titus Barik, Alan Leung, Fred Hohman, Jeffrey Nichols:
BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks. CoRR abs/2404.07387 (2024) - [i27]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:
Apple Intelligence Foundation Language Models. CoRR abs/2407.21075 (2024) - [i26]Angie Boggust, Venkatesh Sivaraman, Yannick Assogba, Donghao Ren, Dominik Moritz, Fred Hohman:
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments. CoRR abs/2408.03274 (2024) - [i25]Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery:
AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking. CoRR abs/2409.18203 (2024) - [i24]Catherine Yeh, Donghao Ren, Yannick Assogba, Dominik Moritz, Fred Hohman:
Exploring Empty Spaces: Human-in-the-Loop Data Augmentation. CoRR abs/2410.01088 (2024) - 2023
- [i23]Aspen K. Hopkins, Fred Hohman, Luca Zappella, Xavier Suau Cuadros, Dominik Moritz:
Designing Data: Proactive Data Collection and Iteration for Machine Learning. CoRR abs/2301.10319 (2023) - [i22]Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery, Fred Hohman, Adriana Hilliard, R. Benjamin Shapiro:
Collaborative Machine Learning Model Building with Families Using Co-ML. CoRR abs/2304.05444 (2023) - [i21]Samantha Robertson, Zijie J. Wang, Dominik Moritz, Mary Beth Kery, Fred Hohman:
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements. CoRR abs/2304.05967 (2023) - [i20]Zijie J. Wang, Fred Hohman, Duen Horng Chau:
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings. CoRR abs/2306.09328 (2023) - [i19]Fred Hohman, Mary Beth Kery, Donghao Ren, Dominik Moritz:
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences. CoRR abs/2310.04621 (2023) - 2022
- [i18]Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz:
Symphony: Composing Interactive Interfaces for Machine Learning. CoRR abs/2202.08946 (2022) - 2021
- [i17]Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P. Wright, Omar Shaikh, Fred Hohman, Duen Horng Chau:
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks. CoRR abs/2108.12931 (2021) - [i16]Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel:
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. CoRR abs/2110.12536 (2021) - 2020
- [i15]Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau:
CNN 101: Interactive Visual Learning for Convolutional Neural Networks. CoRR abs/2001.02004 (2020) - [i14]Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau:
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning. CoRR abs/2001.07769 (2020) - [i13]Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau:
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. CoRR abs/2004.15004 (2020) - [i12]Nilaksh Das, Haekyu Park, Zijie J. Wang, Fred Hohman, Robert Firstman, Emily Rogers, Duen Horng Chau:
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks. CoRR abs/2009.02608 (2020) - [i11]Mary Beth Kery, Donghao Ren, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Kayur Patel:
mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks. CoRR abs/2009.10643 (2020) - 2019
- [i10]Fred Hohman, Haekyu Park, Caleb Robinson, Duen Horng Chau:
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations. CoRR abs/1904.02323 (2019) - [i9]Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau:
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning. CoRR abs/1904.05419 (2019) - [i8]Haekyu Park, Fred Hohman, Duen Horng Chau:
NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions. CoRR abs/1906.00332 (2019) - [i7]Xiangyun Lei, Fred Hohman, Duen Horng Chau, Andrew J. Medford:
ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features. CoRR abs/1908.08381 (2019) - 2018
- [i6]Fred Hohman, Minsuk Kahng, Robert S. Pienta, Duen Horng Chau:
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers. CoRR abs/1801.06889 (2018) - [i5]Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau:
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression. CoRR abs/1802.06816 (2018) - [i4]Ángel Alexander Cabrera, Fred Hohman, Jason Lin, Duen Horng Chau:
Interactive Classification for Deep Learning Interpretation. CoRR abs/1806.05660 (2018) - [i3]James Abello, Fred Hohman, Varun Bezzam, Duen Horng Chau:
Large Graph Exploration via Subgraph Discovery and Decomposition. CoRR abs/1808.04414 (2018) - 2017
- [i2]Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E. Kounavis, Duen Horng Chau:
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression. CoRR abs/1705.02900 (2017) - [i1]Caleb Robinson, Fred Hohman, Bistra Dilkina:
A Deep Learning Approach for Population Estimation from Satellite Imagery. CoRR abs/1708.09086 (2017)
Coauthor Index
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last updated on 2024-12-12 20:59 CET by the dblp team
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