We propose a self-supervised capsule architecture for 3D point clouds.

The Job Shop Scheduling (JSS) problem can be viewed as an integer optimization program with linear objective function and linear, disjunctive constraints. The Lagrangian-based deep learning model does not necessarily produce feasible schedules directly. The model presented below is used to construct solutions that are integral, and feasible to the original problem constraints. The experimental setting, as defined by the training and test data, simulates a situation in which some component of a manufacturing system'slows down', causing processing times to extend on The model training follows the selection of parameters presented in Table 3.Parameter V alue Parameter V alue Epochs 500 Batch Size 16 Learning rate [1 . Finally, Constraints (23) capture Kirchho ff's Current Law and Constraints (24) capture Ohm's Law.

We then apply the framework to the challenging practical setting where confounding factors (that induce spurious correlations) are observable only on a small fraction of data.

Meta-learning approaches have emerged as a popular solution to achieve this. However, meta-reinforcement learning (meta-RL) algorithms have thus far been predominately validated on simple environments with narrow task distributions.

This defense ability, however, is challenged in this paper.

Scientists and engineers have been working on approximating the governing PDEs of these physical systems for centuries. The emergence of the computer-aided simulation facilitates a cost-friendly way to study these challenging problems.

On a number of common benchmark tasks to recover underlying expressions from a dataset, our method recovers 65% more expressions than a recently published top-performing model using the same experimental setup.

Although FG has shown reasonable performance for the given input, as the shortage of the global property, FG is vulnerable to the small perturbation, while IG which includes the exploration over the input space is robust.