Therefore a reasonable alternative is to be able to automatically generate new annotated training samples using a process known as data augmentation.

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

The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input.

In this paper, we show how to combine the favourable aspects of these two approaches.

Deep neural networks (DNNs) and probabilistic graphical models (PGMs) are the two main tools for statistical modeling.

Unfortunately, if cross-task knowledge transfer is impossible, then we will overfit each task due to limited amount of labeled data. One way to circumvent this dilemma is to use an external data source, e.g.

We propose a novel attention mechanism for implementing the covert attention. Here, the architecture is used for multi-label image classifica-31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

Few-shot learning refers to understanding new concepts from only a few examples.

Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.

A modified Gate Recurrent Unit is employed in GAN to model the temporal irregularity of the incomplete time series.