Statistical Learning
Revisiting the Integration of Convolution and Attention for Vision Backbone
Convolutions (Convs) and multi-head self-attentions (MHSAs) are typically considered alternatives to each other for building vision backbones. Although some works try to integrate both, they apply the two operators simultaneously at the finest pixel granularity.
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families
Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant
Neural Information Processing Systems http://nips.cc/
7716d0fc31636914783865d34f6cdfd5-AuthorFeedback.pdf
This is becausea>t a takes a large amount of iterations to increase from negative to0.26 Consequently,withalargestepsize,wcanmovefarawayfromw beforea>t a becomesnonnegative. For problems with multiple global optima, our analysis can still be applied if the35 following condition holds: there exists one global optimum such that the PD condition holds globally with respect36 tothis optimum.
Online Estimation via Offline Estimation: An Information-Theoretic Framework Dylan J. Foster
The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary theory of online learning provides algorithms for estimation under adaptively
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Neural Information Processing Systems http://nips.cc/
