Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Elsayed, Gamaleldin F."'
Autor:
Freeman, C. Daniel, Culp, Laura, Parisi, Aaron, Bileschi, Maxwell L, Elsayed, Gamaleldin F, Rizkowsky, Alex, Simpson, Isabelle, Alemi, Alex, Nova, Azade, Adlam, Ben, Bohnet, Bernd, Mishra, Gaurav, Sedghi, Hanie, Mordatch, Igor, Gur, Izzeddin, Lee, Jaehoon, Co-Reyes, JD, Pennington, Jeffrey, Xu, Kelvin, Swersky, Kevin, Mahajan, Kshiteej, Xiao, Lechao, Liu, Rosanne, Kornblith, Simon, Constant, Noah, Liu, Peter J., Novak, Roman, Qian, Yundi, Fiedel, Noah, Sohl-Dickstein, Jascha
We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic questions posed in natural language, with an arbitrary adversarial str
Externí odkaz:
http://arxiv.org/abs/2311.07587
Autor:
Dehghani, Mostafa, Djolonga, Josip, Mustafa, Basil, Padlewski, Piotr, Heek, Jonathan, Gilmer, Justin, Steiner, Andreas, Caron, Mathilde, Geirhos, Robert, Alabdulmohsin, Ibrahim, Jenatton, Rodolphe, Beyer, Lucas, Tschannen, Michael, Arnab, Anurag, Wang, Xiao, Riquelme, Carlos, Minderer, Matthias, Puigcerver, Joan, Evci, Utku, Kumar, Manoj, van Steenkiste, Sjoerd, Elsayed, Gamaleldin F., Mahendran, Aravindh, Yu, Fisher, Oliver, Avital, Huot, Fantine, Bastings, Jasmijn, Collier, Mark Patrick, Gritsenko, Alexey, Birodkar, Vighnesh, Vasconcelos, Cristina, Tay, Yi, Mensink, Thomas, Kolesnikov, Alexander, Pavetić, Filip, Tran, Dustin, Kipf, Thomas, Lučić, Mario, Zhai, Xiaohua, Keysers, Daniel, Harmsen, Jeremiah, Houlsby, Neil
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image an
Externí odkaz:
http://arxiv.org/abs/2302.05442
Autor:
Biza, Ondrej, van Steenkiste, Sjoerd, Sajjadi, Mehdi S. M., Elsayed, Gamaleldin F., Mahendran, Aravindh, Kipf, Thomas
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about objects in a self-supervised manner have made exciting progress in this di
Externí odkaz:
http://arxiv.org/abs/2302.04973
Autor:
Elsayed, Gamaleldin F., Mahendran, Aravindh, van Steenkiste, Sjoerd, Greff, Klaus, Mozer, Michael C., Kipf, Thomas
The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision approaches unless exp
Externí odkaz:
http://arxiv.org/abs/2206.07764
Autor:
Kipf, Thomas, Elsayed, Gamaleldin F., Mahendran, Aravindh, Stone, Austin, Sabour, Sara, Heigold, Georg, Jonschkowski, Rico, Dosovitskiy, Alexey, Greff, Klaus
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Recent work on simple 2D and 3D datasets has shown that models with objec
Externí odkaz:
http://arxiv.org/abs/2111.12594
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes. This is especially true for dermatology, a specialty with thousands of skin condit
Externí odkaz:
http://arxiv.org/abs/2010.04308
Publikováno v:
International Conference on Machine Learning, 2020
Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias. Locally connected layers, which differ from convo
Externí odkaz:
http://arxiv.org/abs/2002.02959
Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret. One approach that offers some level of interpretability by design is \textit{ha
Externí odkaz:
http://arxiv.org/abs/1908.07644
Publikováno v:
International Conference on Learning Representations 2019
Deep neural networks are susceptible to \emph{adversarial} attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been de
Externí odkaz:
http://arxiv.org/abs/1806.11146
We present a formulation of deep learning that aims at producing a large margin classifier. The notion of margin, minimum distance to a decision boundary, has served as the foundation of several theoretically profound and empirically successful resul
Externí odkaz:
http://arxiv.org/abs/1803.05598