Government
A for FLAIR
Unqualified images are removed as described in Appendix A.3. Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to
Triad Constraints for Learning Causal Structure of Latent Variables
Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang
Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables).
End to end learning and optimization on graphs
Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe
Neural Information Processing Systems http://nips.cc/
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
Aiming to address this limitation, we present Easy2Hard-Bench, a consistently formatted collection of 6 benchmark datasets spanning various domains, such as mathematics and programming problems, chess puzzles, and reasoning questions.
A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training
However, the training of a sparse DNN encounters great challenges in achieving optimal generalization ability despite the efforts from the state-of-the-art sparse training methodologies. To unravel the mysterious reason behind the difficulty of sparse training, we connect network sparsity with the structure of neural loss functions and identify that the cause of such difficulty lies in a chaotic loss surface.
Accurate, reliable and fast robustness evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
Neural Information Processing Systems http://nips.cc/
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Mehmet Fatih Sahin, Armin eftekhari, Ahmet Alacaoglu, Fabian Latorre, Volkan Cevher
Neural Information Processing Systems http://nips.cc/
Waymo Asks the DC Public to Pressure Their City Officials
Stuck in regulatory limbo, the self-driving-vehicle developer is encouraging residents of Washington, DC, to message public officials to help get its robotaxis onto roads. Waymo needs some help, according to an email message the self-driving developer sent to residents of Washington, DC, on Thursday. For more than a year, Waymo has been pushing city officials to pass new regulations allowing its robotaxis to operate in the district. So far, self-driving cars can test in the city with humans behind the wheel, but cannot operate in driver-free mode. The Alphabet subsidiary--and its lobbyists--have asked local lawmakers, including Mayor Muriel Bower and members of the city council, to create new rules allowing the tech to go truly driverless on its public roads.
