Large Margin Deep Networks for Classification. - Gamaleldin F. Elsayed; Dilip Krishnan; Hossein Mobahi; Kevin Regan 001; Samy Bengio (NeurIPS, 2018)

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. - Gamaleldin F. Elsayed; Shreya Shankar; Brian Cheung; Nicolas Papernot; Alexey Kurakin; Ian J. Goodfellow; Jascha Sohl-Dickstein (NeurIPS, 2018)

Adversarial Examples that Fool both Human and Computer Vision. - Gamaleldin F. Elsayed; Shreya Shankar; Brian Cheung; Nicolas Papernot; Alex Kurakin; Ian J. Goodfellow; Jascha Sohl-Dickstein (CoRR, 2018)

Adversarial Reprogramming of Neural Networks. - Gamaleldin F. Elsayed; Ian J. Goodfellow; Jascha Sohl-Dickstein (ICLR (Poster), 2019)

Saccader: Improving Accuracy of Hard Attention Models for Vision. - Gamaleldin F. Elsayed; Simon Kornblith; Quoc V. Le (NeurIPS, 2019)

Revisiting Spatial Invariance with Low-Rank Local Connectivity. - Gamaleldin F. Elsayed; Prajit Ramachandran; Jonathon Shlens; Simon Kornblith (ICML, 2020)

Addressing the Real-world Class Imbalance Problem in Dermatology. - Wei-Hung Weng; Jonathan Deaton; Vivek Natarajan; Gamaleldin F. Elsayed; Yuan Liu (ML4H@NeurIPS, 2020)

Conditional Object-Centric Learning from Video. - Thomas Kipf; Gamaleldin Fathy Elsayed; Aravindh Mahendran; Austin Stone; Sara Sabour; Georg Heigold; Rico Jonschkowski; Alexey Dosovitskiy; Klaus Greff (ICLR, 2022)

SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. - Gamaleldin F. Elsayed; Aravindh Mahendran; Sjoerd van Steenkiste; Klaus Greff; Michael C. Mozer; Thomas Kipf (NeurIPS, 2022)

Teacher-generated spatial-attention labels boost robustness and accuracy of contrastive models. - Yushi Yao; Chang Ye; Junfeng He; Gamaleldin F. Elsayed (CVPR, 2023)

Learning in temporally structured environments. - Matt Jones 001; Tyler R. Scott; Mengye Ren; Gamaleldin Fathy Elsayed; Katherine L. Hermann; David Mayo; Michael Curtis Mozer (ICLR, 2023)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames. - Ondrej Biza; Sjoerd van Steenkiste; Mehdi S. M. Sajjadi; Gamaleldin Fathy Elsayed; Aravindh Mahendran; Thomas Kipf (ICML, 2023)

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? - C. Daniel Freeman; Laura Culp; Aaron Parisi; Maxwell L. Bileschi; Gamaleldin F. Elsayed; Alex Rizkowsky; Isabelle Simpson; Alex Alemi; Azade Nova; Ben Adlam; Bernd Bohnet; Gaurav Mishra; et al. (CoRR, 2023)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. - Jiri Hron; Laura Culp; Gamaleldin F. Elsayed; Rosanne Liu; Ben Adlam; Maxwell L. Bileschi; Bernd Bohnet; JD Co-Reyes; Noah Fiedel; C. Daniel Freeman; Izzeddin Gur; Kathleen Kenealy; et al. (CoRR, 2024)

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. - Avi Singh; John D. Co-Reyes; Rishabh Agarwal; Ankesh Anand; Piyush Patil; Xavier Garcia; Peter J. Liu; James Harrison; Jaehoon Lee 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)