Statistical Learning
perform in-context learning, it is unclear what the relationship is between tasks on which this succeeds and what is present in the training data. To make progress
In-context learning refers to the ability of a model to condition on a prompt sequence consisting of in-context examples (input-output pairs from some task) along with a new query input, and generate the corresponding output.
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds Le Hui
To this end, we first perform template feature embedding to embed the template's feature into
Learning a Single Neuron with Bias Using Gradient Descent Gal Vardi
In this work, we study the common setting of learning a single neuron with respect to the squared loss, using gradient descent.
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity
In this work we bridge these two approaches under a strict complementarity assumption, which in particular implies that the optimal solution to the convex relaxation is unique and is also the optimal solution to the original nonconvex problem.
Supplementary material for MIME Contents of Appendix
A naive implementation of this method requires two steps of communication per round to implement this algorithm.
