David Bau

131 papers A* 24A 3B 5C 1Misc 1Journal 80Unranked 14
YearRankTypeTitle / Venue / Authors
2026 J jnl
Agents of Chaos.
CoRR
Natalie Shapira, Chris Wendler, Avery Yen, Gabriele Sarti, Koyena Pal, Olivia Floody, Adam Belfki, Alexander R. Loftus, Aditya Ratan Jannali, Nikhil Prakash, Jasmine Cui, Giordano Rogers, Jannik Brinkmann, Can Rager, Amir Zur, Michael Ripa, Aruna Sankaranarayanan, David Atkinson, Rohit Gandikota, Jaden Fiotto-Kaufman, Eunjeong Hwang, Hadas Orgad, P. Sam Sahil, Negev Taglicht, Tomer Shabtay, Atai Ambus, Nitay Alon, Shiri Oron, Ayelet Gordon-Tapiero, Yotam Kaplan, Vered Shwartz, Tamar Rott Shaham, Christoph Riedl, Reuth Mirsky, Maarten Sap, David Manheim, Tomer Ullman, David Bau
2026 J jnl
Do explanations generalize across large reasoning models?
CoRR
Koyena Pal, David Bau, Chandan Singh
2026 J jnl
Mechanisms of AI Protein Folding in ESMFold.
CoRR
Kevin Lu, Jannik Brinkmann, Stefan Huber, Aaron Mueller, Yonatan Belinkov, David Bau, Chris Wendler
2026 J jnl
The Dual Mechanisms of Spatial Reasoning in Vision-Language Models.
CoRR
Kelly Cui, Nikhil Prakash, Ayush Raina, David Bau, Antonio Torralba, Tamar Rott Shaham
2025 J jnl
Discovering Forbidden Topics in Language Models.
CoRR
Can Rager, Chris Wendler, Rohit Gandikota, David Bau
2025 J jnl
Distilling Diversity and Control in Diffusion Models.
CoRR
Rohit Gandikota, David Bau
2025 — conf
Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare.
EMNLP (Findings)
Hiba Ahsan, Arnab Sen Sharma, Silvio Amir, David Bau, Byron C. Wallace
2025 J jnl
Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare.
CoRR
Hiba Ahsan, Arnab Sen Sharma, Silvio Amir, David Bau, Byron C. Wallace
2025 — conf
Hidden Layers: An Interactive Installation for Exploring the Neural Semantics of Image Synthesis.
Aarhus Conference (Adjunct)
Imke Grabe, Jaden Fiotto-Kaufman, David Bau, Tom Jenkins
2025 J jnl
In-Context Algebra.
CoRR
Eric Todd, Jannik Brinkmann, Rohit Gandikota, David Bau
2025 J jnl
In-Context Learning Without Copying.
CoRR
Kerem Sahin, Sheridan Feucht, Adam Belfki, Jannik Brinkmann, Aaron Mueller, David Bau, Chris Wendler
2025 J jnl
LLMs Encode Harmfulness and Refusal Separately.
CoRR
Jiachen Zhao, Jing Huang, Zhengxuan Wu, David Bau, Weiyan Shi
2025 J jnl
LLMs Process Lists With General Filter Heads.
CoRR
Arnab Sen Sharma, Giordano Rogers, Natalie Shapira, David Bau
2025 J jnl
Language Models use Lookbacks to Track Beliefs.
CoRR
Nikhil Prakash, Natalie Shapira, Arnab Sen Sharma, Christoph Riedl, Yonatan Belinkov, Tamar Rott Shaham, David Bau, Atticus Geiger
2025 C conf
Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions.
ISC
Keita Teranishi, Harshitha Menon, William F. Godoy, Prasanna Balaprakash, David Bau, Tal Ben-Nun, Abhinav Bhatele, Franz Franchetti, Michael E. Franusich, Todd Gamblin, Giorgis Georgakoudis, Tom Goldstein, Arjun Guha, Steven E. Hahn, Costin Iancu, Zheming Jin, Terry R. Jones, Tze Meng Low, Het Mankad, Narasinga Rao Miniskar, Mohammad Alaul Haque Monil, Daniel Nichols, Konstantinos Parasyris, Swaroop Pophale, Pedro Valero-Lara, Jeffrey S. Vetter, Samuel Williams, Aaron R. Young
2025 J jnl
Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions.
CoRR
Keita Teranishi, Harshitha Menon, William F. Godoy, Prasanna Balaprakash, David Bau, Tal Ben-Nun, Abhinav Bhatele, Franz Franchetti, Michael E. Franusich, Todd Gamblin, Giorgis Georgakoudis, Tom Goldstein, Arjun Guha, Steven E. Hahn, Costin Iancu, Zheming Jin, Terry R. Jones, Tze Meng Low, Het Mankad, Narasinga Rao Miniskar, Mohammad Alaul Haque Monil, Daniel Nichols, Konstantinos Parasyris, Swaroop Pophale, Pedro Valero-Lara, Jeffrey S. Vetter, Samuel Williams, Aaron R. Young
2025 A* conf
MIB: A Mechanistic Interpretability Benchmark.
ICML
Aaron Mueller, Atticus Geiger, Sarah Wiegreffe, Dana Arad, Iván Arcuschin, Adam Belfki, Yik Siu Chan, Jaden Fried Fiotto-Kaufman, Tal Haklay, Michael Hanna, Jing Huang, Rohan Gupta, Yaniv Nikankin, Hadas Orgad, Nikhil Prakash, Anja Reusch, Aruna Sankaranarayanan, Shun Shao, Alessandro Stolfo, Martin Tutek, Amir Zur, David Bau, Yonatan Belinkov
2025 J jnl
MIB: A Mechanistic Interpretability Benchmark.
CoRR
Aaron Mueller, Atticus Geiger, Sarah Wiegreffe, Dana Arad, Iván Arcuschin, Adam Belfki, Yik Siu Chan, Jaden Fiotto-Kaufman, Tal Haklay, Michael Hanna, Jing Huang, Rohan Gupta, Yaniv Nikankin, Hadas Orgad, Nikhil Prakash, Anja Reusch, Aruna Sankaranarayanan, Shun Shao, Alessandro Stolfo, Martin Tutek, Amir Zur, David Bau, Yonatan Belinkov
2025 B conf
Model Lakes.
EDBT
Koyena Pal, David Bau, Renée J. Miller
2025 A* conf
NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals.
ICLR
Jaden Fried Fiotto-Kaufman, Alexander Russell Loftus, Eric Todd, Jannik Brinkmann, Koyena Pal, Dmitrii Troitskii, Michael Ripa, Adam Belfki, Can Rager, Caden Juang, Aaron Mueller, Samuel Marks, Arnab Sen Sharma, Francesca Lucchetti, Nikhil Prakash, Carla E. Brodley, Arjun Guha, Jonathan Bell, Byron C. Wallace, David Bau
2025 J jnl
Open Problems in Mechanistic Interpretability.
CoRR
Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Isaac Bloom, Stella Biderman, Adrià Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, Eric J. Michaud, Stephen Casper, Max Tegmark, William Saunders, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Tom McGrath
2025 J jnl
Open Problems in Mechanistic Interpretability.
Trans. Mach. Learn. Res.
Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeffrey Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Isaac Bloom, Stella Biderman, Adrià Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, William Saunders, Eric J. Michaud, Stephen Casper, Max Tegmark, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Tom McGrath
2025 J jnl
Open Problems in Technical AI Governance.
Trans. Mach. Learn. Res.
Anka Reuel, Benjamin Bucknall, Stephen Casper, Timothy Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, David Bau, Paul Bricman, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, Robert Trager
2025 — conf
Position-aware Automatic Circuit Discovery.
ACL (1)
Tal Haklay, Hadas Orgad, David Bau, Aaron Mueller, Yonatan Belinkov
2025 J jnl
Position-aware Automatic Circuit Discovery.
CoRR
Tal Haklay, Hadas Orgad, David Bau, Aaron Mueller, Yonatan Belinkov
2025 J jnl
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models.
CoRR
Rohit Gandikota, Zongze Wu, Richard Zhang, David Bau, Eli Shechtman, Nicholas I. Kolkin
2025 A* conf
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.
ICLR
Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller
2025 J jnl
The Dual-Route Model of Induction.
CoRR
Sheridan Feucht, Eric Todd, Byron C. Wallace, David Bau
2025 J jnl
Vector Arithmetic in Concept and Token Subspaces.
CoRR
Sheridan Feucht, Byron C. Wallace, David Bau
2025 J jnl
When Are Concepts Erased From Diffusion Models?
CoRR
Kevin Lu, Nicky Kriplani, Rohit Gandikota, Minh Pham, David Bau, Chinmay Hegde, Niv Cohen
2024 J jnl
Art-Free Generative Models: Art Creation Without Graphic Art Knowledge.
CoRR
Hui Ren, Joanna Materzynska, Rohit Gandikota, David Bau, Antonio Torralba
2024 — conf
Black-Box Access is Insufficient for Rigorous AI Audits.
FAccT
Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas A. Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell
2024 J jnl
Black-Box Access is Insufficient for Rigorous AI Audits.
CoRR
Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Alexander Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell
2024 — conf
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models.
ECCV (40)
Rohit Gandikota, Joanna Materzynska, Tingrui Zhou, Antonio Torralba, David Bau
2024 A* conf
Customizing Text-to-Image Models with a Single Image Pair.
SIGGRAPH Asia
Maxwell Jones, Sheng-Yu Wang, Nupur Kumari, David Bau, Jun-Yan Zhu
2024 J jnl
Customizing Text-to-Image Models with a Single Image Pair.
CoRR
Maxwell Jones, Sheng-Yu Wang, Nupur Kumari, David Bau, Jun-Yan Zhu
2024 J jnl
Erasing Conceptual Knowledge from Language Models.
CoRR
Rohit Gandikota, Sheridan Feucht, Samuel Marks, David Bau
2024 A* conf
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking.
ICLR
Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, David Bau
2024 J jnl
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking.
CoRR
Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, David Bau
2024 A* conf
Function Vectors in Large Language Models.
ICLR
Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, David Bau
2024 A* conf
Linearity of Relation Decoding in Transformer Language Models.
ICLR
Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau
2024 J jnl
Locating and Editing Factual Associations in Mamba.
CoRR
Arnab Sen Sharma, David Atkinson, David Bau
2024 J jnl
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice.
CoRR
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando A. Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna M. Wallach, Nicolas Papernot, Katherine Lee
2024 A* conf
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models.
NeurIPS
Adam Karvonen, Benjamin Wright, Can Rager, Rico Angell, Jannik Brinkmann, Logan Smith, Claudio Mayrink Verdun, David Bau, Samuel Marks
2024 J jnl
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models.
CoRR
Adam Karvonen, Benjamin Wright, Can Rager, Rico Angell, Jannik Brinkmann, Logan Smith, Claudio Mayrink Verdun, David Bau, Samuel Marks
2024 J jnl
Measuring and Controlling Persona Drift in Language Model Dialogs.
CoRR
Kenneth Li, Tianle Liu, Naomi Bashkansky, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg
2024 J jnl
Model Lakes.
CoRR
Koyena Pal, David Bau, Renée J. Miller
2024 J jnl
NNsight and NDIF: Democratizing Access to Foundation Model Internals.
CoRR
Jaden Fiotto-Kaufman, Alexander R. Loftus, Eric Todd, Jannik Brinkmann, Caden Juang, Koyena Pal, Can Rager, Aaron Mueller, Samuel Marks, Arnab Sen Sharma, Francesca Lucchetti, Michael Ripa, Adam Belfki, Nikhil Prakash, Sumeet Multani, Carla E. Brodley, Arjun Guha, Jonathan Bell, Byron C. Wallace, David Bau
2024 J jnl
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.
CoRR
Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller
2024 J jnl
The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability.
CoRR
Aaron Mueller, Jannik Brinkmann, Millicent L. Li, Samuel Marks, Koyena Pal, Nikhil Prakash, Can Rager, Aruna Sankaranarayanan, Arnab Sen Sharma, Jiuding Sun, Eric Todd, David Bau, Yonatan Belinkov
2024 A* conf
Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs.
EMNLP
Sheridan Feucht, David Atkinson, Byron C. Wallace, David Bau
2024 J jnl
Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs.
CoRR
Sheridan Feucht, David Atkinson, Byron C. Wallace, David Bau
2024 A conf
Unified Concept Editing in Diffusion Models.
WACV
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzynska, David Bau
2023 J jnl
A Function Interpretation Benchmark for Evaluating Interpretability Methods.
CoRR
Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba
2023 J jnl
An Alternative to Regulation: The Case for Public AI.
CoRR
Nicholas Vincent, David Bau, Sarah Schwettmann, Joshua Tan
2023 J jnl
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models.
CoRR
Rohit Gandikota, Joanna Materzynska, Tingrui Zhou, Antonio Torralba, David Bau
2023 A* conf
Content-based Search for Deep Generative Models.
SIGGRAPH Asia
Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, Mia Tang, David Bau, Jun-Yan Zhu
2023 J jnl
Discovering Variable Binding Circuitry with Desiderata.
CoRR
Xander Davies, Max Nadeau, Nikhil Prakash, Tamar Rott Shaham, David Bau
2023 A* conf
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task.
ICLR
Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg
2023 A* conf
Erasing Concepts from Diffusion Models.
ICCV
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, David Bau
2023 J jnl
Erasing Concepts from Diffusion Models.
CoRR
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, David Bau
2023 A* conf
FIND: A Function Description Benchmark for Evaluating Interpretability Methods.
NeurIPS
Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba
2023 J jnl
Function Vectors in Large Language Models.
CoRR
Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, David Bau
2023 B conf
Future Lens: Anticipating Subsequent Tokens from a Single Hidden State.
CoNLL
Koyena Pal, Jiuding Sun, Andrew Yuan, Byron C. Wallace, David Bau
2023 J jnl
Future Lens: Anticipating Subsequent Tokens from a Single Hidden State.
CoRR
Koyena Pal, Jiuding Sun, Andrew Yuan, Byron C. Wallace, David Bau
2023 J jnl
Linearity of Relation Decoding in Transformer Language Models.
CoRR
Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau
2023 A* conf
Mass-Editing Memory in a Transformer.
ICLR
Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, David Bau
2023 — conf
Multimodal Neurons in Pretrained Text-Only Transformers.
ICCV (Workshops)
Sarah Schwettmann, Neil Chowdhury, Samuel Klein, David Bau, Antonio Torralba
2023 J jnl
Testing Language Model Agents Safely in the Wild.
CoRR
Silen Naihin, David Atkinson, Marc Green, Merwane Hamadi, Craig Swift, Douglas Schonholtz, Adam Tauman Kalai, David Bau
2023 J jnl
Testing methods of neural systems understanding.
Cogn. Syst. Res.
Grace W. Lindsay, David Bau
2023 J jnl
Unified Concept Editing in Diffusion Models.
CoRR
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzynska, David Bau
2022 J jnl
Content-Based Search for Deep Generative Models.
CoRR
Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, David Bau, Jun-Yan Zhu
2022 A* conf
Disentangling visual and written concepts in CLIP.
CVPR
Joanna Materzynska, Antonio Torralba, David Bau
2022 J jnl
Disentangling visual and written concepts in CLIP.
CoRR
Joanna Materzynska, Antonio Torralba, David Bau
2022 J jnl
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task.
CoRR
Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg
2022 — conf
HAI-GEN 2022: 3rd Workshop on Human-AI Co-Creation with Generative Models.
IUI Companion
Justin D. Weisz, Mary Lou Maher, Hendrik Strobelt, Lydia B. Chilton, David Bau, Werner Geyer
2022 J jnl
Local Relighting of Real Scenes.
CoRR
Audrey Cui, Ali Jahanian, Àgata Lapedriza, Antonio Torralba, Shahin Mahdizadehaghdam, Rohit Kumar, David Bau
2022 A* conf
Locating and Editing Factual Associations in GPT.
NeurIPS
Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov
2022 J jnl
Locating and Editing Factual Knowledge in GPT.
CoRR
Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov
2022 J jnl
Mass-Editing Memory in a Transformer.
CoRR
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, David Bau
2022 A* conf
Natural Language Descriptions of Deep Visual Features.
ICLR
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, Jacob Andreas
2022 J jnl
Natural Language Descriptions of Deep Visual Features.
CoRR
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, Jacob Andreas
2022 J jnl
Rewriting Geometric Rules of a GAN.
CoRR
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
2022 J jnl
Rewriting geometric rules of a GAN.
ACM Trans. Graph.
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
2021 — —
Dissection of Deep Neural Networks.
David Bau
2021 A* conf
Editing a classifier by rewriting its prediction rules.
NeurIPS
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry
2021 J jnl
Editing a classifier by rewriting its prediction rules.
CoRR
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry
2021 B conf
From Droplet to Lilypad: Present and Future of Dual-Modality Environments.
VL/HCC
Emma Andrews, David Bau, Jeremiah J. Blanchard
2021 J jnl
Paint by Word.
CoRR
David Bau, Alex Andonian, Audrey Cui, YeonHwan Park, Ali Jahanian, Aude Oliva, Antonio Torralba
2021 A* conf
Sketch Your Own GAN.
ICCV
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
2021 J jnl
Sketch Your Own GAN.
CoRR
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
2021 A* conf
Toward a Visual Concept Vocabulary for GAN Latent Space.
ICCV
Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, Antonio Torralba
2021 J jnl
Toward a Visual Concept Vocabulary for GAN Latent Space.
CoRR
Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, Antonio Torralba
2020 A* conf
Diverse Image Generation via Self-Conditioned GANs.
CVPR
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba
2020 J jnl
Diverse Image Generation via Self-Conditioned GANs.
CoRR
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba
2020 — conf
Rewriting a Deep Generative Model.
ECCV (1)
David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba
2020 J jnl
Rewriting a Deep Generative Model.
CoRR
David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba
2020 J jnl
Semantic Photo Manipulation with a Generative Image Prior.
CoRR
David Bau, Hendrik Strobelt, William S. Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, Antonio Torralba
2020 J jnl
Understanding the Role of Individual Units in a Deep Neural Network.
CoRR
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Àgata Lapedriza, Bolei Zhou, Antonio Torralba
2020 J jnl
Understanding the role of individual units in a deep neural network.
Proc. Natl. Acad. Sci. USA
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Àgata Lapedriza, Bolei Zhou, Antonio Torralba
2020 — conf
What Makes Fake Images Detectable? Understanding Properties that Generalize.
ECCV (26)
Lucy Chai, David Bau, Ser-Nam Lim, Phillip Isola
2020 J jnl
What makes fake images detectable? Understanding properties that generalize.
CoRR
Lucy Chai, David Bau, Ser-Nam Lim, Phillip Isola
2019 — ch.
Comparing the Interpretability of Deep Networks via Network Dissection.
Explainable AI
Bolei Zhou, David Bau, Aude Oliva, Antonio Torralba
2019 J jnl
Dissecting Pruned Neural Networks.
CoRR
Jonathan Frankle, David Bau
2019 — conf
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks.
ICLR (Poster)
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
2019 J jnl
Interpreting Deep Visual Representations via Network Dissection.
IEEE Trans. Pattern Anal. Mach. Intell.
Bolei Zhou, David Bau, Aude Oliva, Antonio Torralba
2019 A* conf
Learning Words by Drawing Images.
CVPR
Didac Suris, Adrià Recasens, David Bau, David Harwath, James R. Glass, Antonio Torralba
2019 A* conf
Seeing What a GAN Cannot Generate.
ICCV
David Bau, Jun-Yan Zhu, Jonas Wulff, William S. Peebles, Bolei Zhou, Hendrik Strobelt, Antonio Torralba
2019 J jnl
Seeing What a GAN Cannot Generate.
CoRR
David Bau, Jun-Yan Zhu, Jonas Wulff, William S. Peebles, Hendrik Strobelt, Bolei Zhou, Antonio Torralba
2019 J jnl
Semantic photo manipulation with a generative image prior.
ACM Trans. Graph.
David Bau, Hendrik Strobelt, William S. Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, Antonio Torralba
2019 J jnl
The cloud is the limit: A case study of programming on the web, with the web.
Int. J. Child Comput. Interact.
David Weintrop, David Bau, Uri Wilensky
2019 — conf
Visualizing and Understanding GANs.
DGS@ICLR
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
2019 J jnl
Visualizing and Understanding Generative Adversarial Networks (Extended Abstract).
CoRR
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
2018 J jnl
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning.
CoRR
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal
2018 B conf
Explaining Explanations: An Overview of Interpretability of Machine Learning.
DSAA
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal
2018 J jnl
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks.
CoRR
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
2018 — conf
Interpretable Basis Decomposition for Visual Explanation.
ECCV (8)
Bolei Zhou, Yiyou Sun, David Bau, Antonio Torralba
2018 J jnl
Revisiting the Importance of Individual Units in CNNs via Ablation.
CoRR
Bolei Zhou, Yiyou Sun, David Bau, Antonio Torralba
2017 J jnl
Interpreting Deep Visual Representations via Network Dissection.
CoRR
Bolei Zhou, David Bau, Aude Oliva, Antonio Torralba
2017 J jnl
Learnable Programming: Blocks and Beyond.
CoRR
David Bau, Jeff Gray, Caitlin Kelleher, Josh Sheldon, Franklyn A. Turbak
2017 J jnl
Learnable programming: blocks and beyond.
Commun. ACM
David Bau, Jeff Gray, Caitlin Kelleher, Josh Sheldon, Franklyn A. Turbak
2017 A* conf
Network Dissection: Quantifying Interpretability of Deep Visual Representations.
CVPR
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba
2017 J jnl
Network Dissection: Quantifying Interpretability of Deep Visual Representations.
CoRR
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba
2015 J jnl
Droplet, a blocks-based editor for text code.
J. Comput. Sci. Coll.
David Bau
2015 Misc conf
Pencil code: block code for a text world.
IDC
David Bau, D. Anthony Bau, Matthew Dawson, C. Sydney Pickens
2015 A conf
Supporting the Computer Science Learning Process.
SIGCSE
Amy Briggs, David Bau, Caroline Meeks, Pat Yongpradit
2015 A conf
Using Pencil Code to Bridge the Gap between Visual and Text-Based Coding (Abstract Only).
SIGCSE
David Bau, Matthew Dawson, Anthony Bau
2008 B conf
Large scale learning and recognition of faces in web videos.
FG
Ming Zhao, Jay Yagnik, Hartwig Adam, David Bau
2001 — conf
Solving Alignment Using Elementary Linear Algebra.
Compiler Optimizations for Scalable Parallel Systems Languages
Vladimir Kotlyar, David Bau, Induprakas Kodukula, Keshav Pingali, Paul Stodghill
1997 — book
Numerical linear algebra.
Lloyd N. Trefethen, David Bau
1994 — conf
Solving Alignment Using Elementary Linear Algebra.
LCPC
David Bau, Induprakas Kodukula, Vladimir Kotlyar, Keshav Pingali, Paul Stodghill