Asia
No-regret Online Learning over Riemannian Manifolds
We establish upper bounds on the regrets of the problem with respect to time horizon, manifold curvature, and manifold dimension.
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng, Xiao Zhang, Faming Liang, Guang Lin
Neural Information Processing Systems http://nips.cc/
A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention
Such investigations have demonstrated that attention layers are able to implement a wide range of different algorithms, even for the same task, using both positional and semantic attributes of the inputs.
OntheSampleComplexityofPrivatelyLearning Axis-Aligned Rectangles
We say that thisprocedure preservesdifferential privacyifno individual'sdata has asignificant effect on the distribution of the outcome of the procedure.
OntheSampleComplexityofPrivatelyLearning Axis-Aligned Rectangles
We say that thisprocedure preservesdifferential privacyifno individual'sdata has asignificant effect on the distribution of the outcome of the procedure.