We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are "big" in terms of the size of recorded data but

Theoretically we show that given the discriminator objective, good semi-supervised learning indeed requires a bad generator, and propose the definition of a preferred generator.

At the heart of deep learning we aim to use neural networks as function approxi-mators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process.

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

One of the drawbacks of INs is scaling with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce V AIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that V AIN is effective for multi-agent predictive modeling.

The ability of predicting the future is important for intelligent systems, e.g.

We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals. Solid harmonic wavelets are computed by multiplying solid harmonic functions with Gaussian windows dilated at different scales. Invariant scattering coefficients are obtained by cascading such wavelet transforms with the complex modulus nonlinearity.

Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI.

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

Even humans cannot predict the precise age from a single facial image. They may say that the person is probably in one age group and less likely to be in another.