Attacking the Madry Defense Model with $L_1$-based Adversarial Examples

Autor: Sharma, Yash, Chen, Pin-Yu

The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model. Attacks were constrained to perturb each pixel of the input image by a scaled maximal $L_\infty$ distortion $\epsilon$ = 0.3. This

Externí odkaz: http://arxiv.org/abs/1710.10733

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GPT-4o System Card

Autor: OpenAI, Hurst, Aaron, Lerer, Adam, Goucher, Adam P., Perelman, Adam, Ramesh, Aditya, Clark, Aidan, Ostrow, AJ, Welihinda, Akila, Hayes, Alan, Radford, Alec, Mądry, Aleksander, Baker-Whitcomb, Alex, Beutel, Alex, Borzunov, Alex, Carney, Alex, Chow, Alex, Kirillov, Alex, Nichol, Alex, Paino, Alex, Renzin, Alex, Passos, Alex Tachard, Kirillov, Alexander, Christakis, Alexi, Conneau, Alexis, Kamali, Ali, Jabri, Allan, Moyer, Allison, Tam, Allison, Crookes, Amadou, Tootoochian, Amin, Tootoonchian, Amin, Kumar, Ananya, Vallone, Andrea, Karpathy, Andrej, Braunstein, Andrew, Cann, Andrew, Codispoti, Andrew, Galu, Andrew, Kondrich, Andrew, Tulloch, Andrew, Mishchenko, Andrey, Baek, Angela, Jiang, Angela, Pelisse, Antoine, Woodford, Antonia, Gosalia, Anuj, Dhar, Arka, Pantuliano, Ashley, Nayak, Avi, Oliver, Avital, Zoph, Barret, Ghorbani, Behrooz, Leimberger, Ben, Rossen, Ben, Sokolowsky, Ben, Wang, Ben, Zweig, Benjamin, Hoover, Beth, Samic, Blake, McGrew, Bob, Spero, Bobby, Giertler, 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GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs

Externí odkaz: http://arxiv.org/abs/2410.21276

ContextCite: Attributing Model Generation to Context

Autor: Cohen-Wang, Benjamin, Shah, Harshay, Georgiev, Kristian, Madry, Aleksander

How do language models use information provided as context when generating a response? Can we infer whether a particular generated statement is actually grounded in the context, a misinterpretation, or fabricated? To help answer these questions, we i

Externí odkaz: http://arxiv.org/abs/2409.00729

Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection

Autor: Jain, Saachi, Hamidieh, Kimia, Georgiev, Kristian, Ilyas, Andrew, Ghassemi, Marzyeh, Madry, Aleksander

Machine learning models can fail on subgroups that are underrepresented during training. While techniques such as dataset balancing can improve performance on underperforming groups, they require access to training group annotations and can end up re

Externí odkaz: http://arxiv.org/abs/2406.16846