David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba. Understanding the role of individual units in a deep neural network. Proceedings of the National Academy of Sciences (PNAS), Volume 117, no. 48, December 1 2020, pp. 30071-30078.

David Bau, Bolei Zhou, Aude Oliva, Antonio Torralba: Interpreting Deep Visual Representations via Network Dissection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Volume 41 Issue 9, September 2019, pp. 2131-2145.

David Bau, Jeff Gray, Caitlin Kelleher, Josh Sheldon, Franklyn Turbak. Learnable Programming: Blocks and Beyond. Communications of the ACM (CACM) Volume 60 Issue 6, June 2017. pp. 72-80.

Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, and Aleksander Madry. Editing a classifier by rewriting its prediction rules. Advances in Neural Information Processing Systems 34. (NeuIPS 2021)

Emma Andrews, David Bau, and Jeremiah Blanchard. From Droplet to Lilypad: Present and Future of Dual-Modality Environments. 2021 IEEE Symposium on Visual Languages and Human-Centric Computing. (VL/HCC 2021)

Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, and Antonio Torralba. Toward a Visual Concept Vocabulary for GAN Latent Space. Proceedings of the IEEE/CVF International Conference on Computer Vision. (ICCV 2021)

Sheng-Yu Wang, David Bau, and Jun-Yan Zhu. Sketch Your Own GAN. Proceedings of the IEEE/CVF International Conference on Computer Vision. (ICCV 2021)

David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, and Antonio Torralba. Rewriting a Deep Generative Model. Proceedings of the European Conference on Computer Vision. (ECCV 2020 oral)

Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? Understanding properties that generalize. Proceedings of the European Conference on Computer Vision. (ECCV 2020)

Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, and Antonio Torralba. Diverse Image Generation via Self-Conditioned GANs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (CVPR 2020)

David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba. Seeing What a GAN Cannot Generate. Proceedings of the IEEE International Conference on Computer Vision, pp. 4502-4511. (ICCV 2019 oral presentation)

David Bau, Hendrik Strobelt, William Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, and Antonio Torralba. Semantic Photo Manipulation with a Generative Image Prior. ACM Transactions on Graphics (TOG) 38, no. 4. (SIGGRAPH 2019)

Didac Suris, Adria Recasens, David Bau, David Harwath, James Glass, and Antonio Torralba. Learning words by drawing images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (CVPR 2019)

David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, and Antonio Torralba. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. Proceedings of the Seventh International Conference on Learning Representations. (ICLR 2019)

Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, Lalana Kagal. Explaining Explanations: An Overview of Interpretability of Machine Learning. Proceedings of the IEEE 5th International Conference on Data Science and Advanced Analytics. (DSAA 2018)

Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba. Interpretable Basis Decomposition for Visual Explanation. Proceedings of the European Conference on Computer Vision. (ECCV 2018)

David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba. Network Dissection: Quantifying Interpretability of Deep Visual Representations. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017 oral presentation)

David Bau, Matt Dawson M, Anthony Bau, C.S. Pickens Pencil Code: Block Code for a Text World. Proceedings of the 14th International Conference on Interaction Design and Children. pp 445-448. (IDC 2015)

Ming Zhao, Jay Yagnik, Hartwig Adam, David Bau. Large Scale Learning and Recognition of Faces in Web Videos. 8th IEEE International Conference on Automatic Face and Gesture Recognition. (FG 2008)

David Bau, Induprakas Kodukula, Vladimir Kotlyar, Keshav Pingali, Paul Stodghill. Solving Alignment Using Elementary Linear Algebra. Languages and Compilers for Parallel Computing, Lecture Notes in Computer Science Volume 892, pp 46-60. (LCPC 1994)

David Bau, Steven Liu, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba Horses With Blue Jeans - Creating New Worlds by Rewriting a GAN. 4th Workshop on Machine Learning for Creativity and Design (NeurIPS 2020 Workshop)

David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba. Inverting Layers of a Large Generator. ICLR Debugging Machine Learning Models Workshop. (ICLR 2019 workshop)

Jonathan Frankle, David Bau. Dissecting Pruned Neural Networks. ICLR Debugging Machine Learning Models Workshop. (ICLR 2019 workshop)

Saksham Aggarwal, David Anthony Bau, David Bau. A blocks-based editor for HTML code. IEEE Blocks and Beyond Workshop, pp. 83-85. (VL/HCC 2015 workshop)

David Bau, Anthony Bau. A Preview of Pencil Code: A Tool for Developing Mastery of Programming. Proceedings of the 2nd Workshop on Programming for Mobile & Touch. (PROMOTO 2014)

Lloyd N. Trefethen, David Bau. Numerical Linear Algebra. (373pp.) Society for Industrial and Applied Mathematics. (1997)