However, all known theoretical rates are worse than the rate of gradient descent, which synchronizes after every gradient step.

However, all known theoretical rates are worse than the rate of gradient descent, which synchronizes after every gradient step.

We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image, focusing on part-level shape reconstruction and pose and kinematics estimation.

Experiments on twelve corrupted MLL datasets show the effectiveness of SMILEoverseveral existing SPMLL approaches.

Distributed multi-task learning provides significant advantages in multi-agent networkswithheterogeneous datasources where agents aimtolearndistinctbut correlated models simultaneously. However, distributed algorithms for learning relatedness among tasks arenotresilient inthepresence ofByzantine agents. In this paper, we present an approach for Byzantine resilient distributed multi-task learning. We propose an efficient online weight assignment rule by measuring the accumulated loss using an agent's data and its neighbors' models. A small accumulated loss indicates a large similarity between the two tasks.

A common approach to this problem is to use a linear approximationVπ(s)=ϕ(s) x,whereϕ(s)isafeaturevectorofastate s.