1 code implementation • 11 Feb 2022 • Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.
no code implementations • 29 Sep 2021 • Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete.
1 code implementation • 13 Aug 2021 • Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
We describe new datasets for studying generalization from easy to hard examples.
no code implementations • 31 Jul 2021 • Zeyad Emam, Andrew Kondrich, Sasha Harrison, Felix Lau, Yushi Wang, Aerin Kim, Elliot Branson
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL).
2 code implementations • NeurIPS Workshop ICBINB 2020 • W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein
The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.