David Bau

Publication Activity (10 Years)

Years Active: 1994-2024
Publications (10 Years): 82

Top Topics

Image Generation

Diffusion Models

Visual Representation

Top Venues

Publications

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
Black-Box Access is Insufficient for Rigorous AI Audits. FAccT (2024)
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
Black-Box Access is Insufficient for Rigorous AI Audits. CoRR (2024)
Sheridan Feucht, David Atkinson, Byron C. Wallace, David Bau
Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs. CoRR (2024)
Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, David Bau
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking. ICLR (2024)
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzynska, David Bau
Unified Concept Editing in Diffusion Models. WACV (2024)
Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, David Bau
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking. CoRR (2024)
Arnab Sen Sharma, David Atkinson, David Bau
Locating and Editing Factual Associations in Mamba. CoRR (2024)
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
NNsight and NDIF: Democratizing Access to Foundation Model Internals. CoRR (2024)
Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, David Bau
Function Vectors in Large Language Models. ICLR (2024)
Kenneth Li, Tianle Liu, Naomi Bashkansky, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg
Measuring and Controlling Persona Drift in Language Model Dialogs. CoRR (2024)
Maxwell Jones, Sheng-Yu Wang, Nupur Kumari, David Bau, Jun-Yan Zhu
Customizing Text-to-Image Models with a Single Image Pair. CoRR (2024)
Koyena Pal, David Bau, Renée J. Miller
Model Lakes. CoRR (2024)
Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models. CoRR (2024)
Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau
Linearity of Relation Decoding in Transformer Language Models. ICLR (2024)
Grace W. Lindsay, David Bau
Testing methods of neural systems understanding. Cogn. Syst. Res. 82 (2023)
Xander Davies, Max Nadeau, Nikhil Prakash, Tamar Rott Shaham, David Bau
Discovering Variable Binding Circuitry with Desiderata. CoRR (2023)
Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task. ICLR (2023)
Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, David Bau
Mass-Editing Memory in a Transformer. ICLR (2023)
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, David Bau
Erasing Concepts from Diffusion Models. ICCV (2023)
Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, Mia Tang, David Bau, Jun-Yan Zhu
Content-based Search for Deep Generative Models. SIGGRAPH Asia (2023)
Koyena Pal, Jiuding Sun, Andrew Yuan, Byron C. Wallace, David Bau
Future Lens: Anticipating Subsequent Tokens from a Single Hidden State. CoRR (2023)
Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, David Bau
Linearity of Relation Decoding in Transformer Language Models. CoRR (2023)
Koyena Pal, Jiuding Sun, Andrew Yuan, Byron C. Wallace, David Bau
Future Lens: Anticipating Subsequent Tokens from a Single Hidden State. CoNLL (2023)
Rohit Gandikota, Joanna Materzynska, Tingrui Zhou, Antonio Torralba, David Bau
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models. CoRR (2023)
Nicholas Vincent, David Bau, Sarah Schwettmann, Joshua Tan
An Alternative to Regulation: The Case for Public AI. CoRR (2023)
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzynska, David Bau
Unified Concept Editing in Diffusion Models. CoRR (2023)
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, David Bau
Erasing Concepts from Diffusion Models. CoRR (2023)
Silen Naihin, David Atkinson, Marc Green, Merwane Hamadi, Craig Swift, Douglas Schonholtz, Adam Tauman Kalai, David Bau
Testing Language Model Agents Safely in the Wild. CoRR (2023)
Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, David Bau
Function Vectors in Large Language Models. CoRR (2023)
Sarah Schwettmann, Neil Chowdhury, Samuel Klein, David Bau, Antonio Torralba
Multimodal Neurons in Pretrained Text-Only Transformers. ICCV (Workshops) (2023)
Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba
A Function Interpretation Benchmark for Evaluating Interpretability Methods. CoRR (2023)
Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba
FIND: A Function Description Benchmark for Evaluating Interpretability Methods. NeurIPS (2023)
Audrey Cui, Ali Jahanian, Àgata Lapedriza, Antonio Torralba, Shahin Mahdizadehaghdam, Rohit Kumar, David Bau
Local Relighting of Real Scenes. CoRR (2022)
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Rewriting geometric rules of a GAN. ACM Trans. Graph. 41 (4) (2022)
Joanna Materzynska, Antonio Torralba, David Bau
Disentangling visual and written concepts in CLIP. CVPR (2022)
Joanna Materzynska, Antonio Torralba, David Bau
Disentangling visual and written concepts in CLIP. CoRR (2022)
Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov
Locating and Editing Factual Associations in GPT. NeurIPS (2022)
Justin D. Weisz, Mary Lou Maher, Hendrik Strobelt, Lydia B. Chilton, David Bau, Werner Geyer
HAI-GEN 2022: 3rd Workshop on Human-AI Co-Creation with Generative Models. IUI Companion (2022)
Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov
Locating and Editing Factual Knowledge in GPT. CoRR (2022)
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, Jacob Andreas
Natural Language Descriptions of Deep Visual Features. ICLR (2022)
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, Jacob Andreas
Natural Language Descriptions of Deep Visual Features. CoRR (2022)
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, David Bau
Mass-Editing Memory in a Transformer. CoRR (2022)
Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task. CoRR (2022)
Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, David Bau, Jun-Yan Zhu
Content-Based Search for Deep Generative Models. CoRR (2022)
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Rewriting Geometric Rules of a GAN. CoRR (2022)
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry
Editing a classifier by rewriting its prediction rules. CoRR (2021)
Emma Andrews, David Bau, Jeremiah J. Blanchard
From Droplet to Lilypad: Present and Future of Dual-Modality Environments. VL/HCC (2021)
David Bau, Alex Andonian, Audrey Cui, YeonHwan Park, Ali Jahanian, Aude Oliva, Antonio Torralba
Paint by Word. CoRR (2021)
Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, Antonio Torralba
Toward a Visual Concept Vocabulary for GAN Latent Space. CoRR (2021)
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry
Editing a classifier by rewriting its prediction rules. NeurIPS (2021)
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Sketch Your Own GAN. CoRR (2021)
Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, Antonio Torralba
Toward a Visual Concept Vocabulary for GAN Latent Space. ICCV (2021)
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Sketch Your Own GAN. ICCV (2021)
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba
Diverse Image Generation via Self-Conditioned GANs. CVPR (2020)
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba
Diverse Image Generation via Self-Conditioned GANs. CoRR (2020)
David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba
Rewriting a Deep Generative Model. ECCV (1) (2020)
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Àgata Lapedriza, Bolei Zhou, Antonio Torralba
Understanding the role of individual units in a deep neural network. Proc. Natl. Acad. Sci. USA 117 (48) (2020)
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Àgata Lapedriza, Bolei Zhou, Antonio Torralba
Understanding the Role of Individual Units in a Deep Neural Network. CoRR (2020)
David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba
Rewriting a Deep Generative Model. CoRR (2020)
Lucy Chai, David Bau, Ser-Nam Lim, Phillip Isola
What makes fake images detectable? Understanding properties that generalize. CoRR (2020)
Lucy Chai, David Bau, Ser-Nam Lim, Phillip Isola
What Makes Fake Images Detectable? Understanding Properties that Generalize. ECCV (26) (2020)
David Bau, Hendrik Strobelt, William S. Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, Antonio Torralba
Semantic Photo Manipulation with a Generative Image Prior. CoRR (2020)
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
Visualizing and Understanding GANs. DGS@ICLR (2019)
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. ICLR (Poster) (2019)
Jonathan Frankle, David Bau
Dissecting Pruned Neural Networks. CoRR (2019)
David Weintrop, David Bau, Uri Wilensky
The cloud is the limit: A case study of programming on the web, with the web. Int. J. Child Comput. Interact. 20 (2019)
David Bau, Hendrik Strobelt, William S. Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, Antonio Torralba
Semantic photo manipulation with a generative image prior. ACM Trans. Graph. 38 (4) (2019)
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
Visualizing and Understanding Generative Adversarial Networks (Extended Abstract). CoRR (2019)
Bolei Zhou, David Bau, Aude Oliva, Antonio Torralba
Interpreting Deep Visual Representations via Network Dissection. IEEE Trans. Pattern Anal. Mach. Intell. 41 (9) (2019)
David Bau, Jun-Yan Zhu, Jonas Wulff, William S. Peebles, Bolei Zhou, Hendrik Strobelt, Antonio Torralba
Seeing What a GAN Cannot Generate. ICCV (2019)
David Bau, Jun-Yan Zhu, Jonas Wulff, William S. Peebles, Hendrik Strobelt, Bolei Zhou, Antonio Torralba
Seeing What a GAN Cannot Generate. CoRR (2019)
Didac Suris, Adrià Recasens, David Bau, David Harwath, James R. Glass, Antonio Torralba
Learning Words by Drawing Images. CVPR (2019)
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. CoRR (2018)
Bolei Zhou, Yiyou Sun, David Bau, Antonio Torralba
Interpretable Basis Decomposition for Visual Explanation. ECCV (8) (2018)
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal
Explaining Explanations: An Overview of Interpretability of Machine Learning. DSAA (2018)
Bolei Zhou, Yiyou Sun, David Bau, Antonio Torralba
Revisiting the Importance of Individual Units in CNNs via Ablation. CoRR (2018)
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. CoRR (2018)
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba
Network Dissection: Quantifying Interpretability of Deep Visual Representations. CoRR (2017)
David Bau, Jeff Gray, Caitlin Kelleher, Josh Sheldon, Franklyn A. Turbak
Learnable Programming: Blocks and Beyond. CoRR (2017)
Bolei Zhou, David Bau, Aude Oliva, Antonio Torralba
Interpreting Deep Visual Representations via Network Dissection. CoRR (2017)
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba
Network Dissection: Quantifying Interpretability of Deep Visual Representations. CVPR (2017)
David Bau, Jeff Gray, Caitlin Kelleher, Josh Sheldon, Franklyn A. Turbak
Learnable programming: blocks and beyond. Commun. ACM 60 (6) (2017)
Amy Briggs, David Bau, Caroline Meeks, Pat Yongpradit
Supporting the Computer Science Learning Process. SIGCSE (2015)
David Bau, D. Anthony Bau, Matthew Dawson, C. Sydney Pickens
Pencil code: block code for a text world. IDC (2015)
David Bau, Matthew Dawson, Anthony Bau
Using Pencil Code to Bridge the Gap between Visual and Text-Based Coding (Abstract Only). SIGCSE (2015)
Ming Zhao, Jay Yagnik, Hartwig Adam, David Bau
Large scale learning and recognition of faces in web videos. FG (2008)
Vladimir Kotlyar, David Bau, Induprakas Kodukula, Keshav Pingali, Paul Stodghill
Solving Alignment Using Elementary Linear Algebra. Compiler Optimizations for Scalable Parallel Systems Languages (2001)
David Bau, Induprakas Kodukula, Vladimir Kotlyar, Keshav Pingali, Paul Stodghill
Solving Alignment Using Elementary Linear Algebra. LCPC (1994)