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Machine Learning. The Science of Selection under Uncertainty

arXiv:2509.21547v1 Announce Type: cross Abstract: Learning, whether natural or artificial, is a process of selection. It starts with a set of candidate options and selects the more successful ones. In the case of machine learning the selection is done based…

Interpretable time series analysis with Gumbel dynamics

arXiv:2509.21578v1 Announce Type: cross Abstract: Switching dynamical systems can model complicated time series data while maintaining interpretability by inferring a finite set of dynamics primitives and explaining different portions of the observed time series with one of these primitives. However,…

Tricks and Plug-ins for Gradient Boosting in Image Classification

arXiv:2507.22842v2 Announce Type: replace Abstract: Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep architectures. However, the large number of layers and millions of parameters often…

Google’s “G” gets a brighter look

Ten years ago, we introduced Google’s signature four-color G to match the new look and feel of our logo. The design update reflected all the ways people interacted with …

Google’s “G” gets a brighter look.

Ten years ago, we introduced Google’s signature four-color G to match the new look and feel of our logo. The design update reflected all the ways people interacted with …

Checklists Are Better Than Reward Models For Aligning Language Models

Language models must be adapted to understand and follow user instructions. Reinforcement learning is widely used to facilitate this — typically using fixed criteria such as “helpfulness” and “harmfulness”. In our work, we instead propose using flexible, instruction-specific criteria as…