In a typical deep-learning network, some signals flow along a forward path through multiple layers of processing units from the input layer to the output, while other signals flow back from the output layer along a feedback path . Forward-path signals perform inference (e.g. they try to infer what objects are

However, today's very deep models have been accompanied by millions of parameters,

Distributing the training of deep neural networks can be tackled from several angles.

Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback.

Our approach builds upon variational autoencoder (V AE) models for sequential data and combines them with recent work on neural image compression.

It is widely known that neural network training is sensitive to the loss that is minimized.

Abstraction reasoning is a long-standing challenge in artificial intelligence.

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

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors.

Distributed learning is central for large-scale training of deep-learning models.