The first attention model is introduced to handle different numbers of roads-lanes; and the second attention model is intended for enabling decision-making with any number of phases in an intersection.
Multi-task learning is a type of transfer learning that trains multiple tasks simultaneously and leverages the shared information between related tasks to improve the generalization performance.
In this paper, we propose a robust feature engineering method, Randomized Union of Locally Linear Subspaces (RULLS).
Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing.
For example, it is observed that with a Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly.
The collection activities are on-going, and we expect to increase the number of complete recordings in the corpus to 30 by June 2014.
An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix.
This paper describes a recursive estimation procedure for multivariate binary densities using orthogonal expansions.