Recognizing human action in videos is a long-standing research problem in computer vision.

Such mistakes can have disastrous and even potentially fatal consequences.

The world model's extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various

Neural Information Processing Systems http://nips.cc/

Recently, distributed training with AllReduce operations gained popularity as well.

Generalization performance is a central goal in machine learning, with explicit generalization strategies needed when training over-parametrized models, like large neural networks. There is growing interest in using multiple, potentially auxiliary tasks, as one strategy towards this goal.

Deep learning has become increasingly popular in both supervised and unsupervised machine learning thanks to its outstanding empirical performance.

Many state-of-the-art classifiers have been shown to fail catastrophically in the presence of small imperceptible but adversarial perturbations.

Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process.

We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games.