We study the problem of conditional generative modeling based on designated semantics or structures.

Optical Character Recognition (OCR) aims to recognize text in natural images.

In this paper, we propose spatial propagation networks for learning the affinity matrix for vision tasks.

Our analysis demonstrates that only a vanishingly small fraction of the function space is reachable after a polynomial number of gradient descent iterations.

Generative adversarial networks (GANs) have great successes on synthesizing data.

Current state-of-the-art methods for automated Knowledge Base (KB) completion use neural link prediction models to learn distributed vector representations of symbols ( i.e. subsymbolic representations)

We introduce a novel scheme to train binary convolutional neural networks (CNNs) - CNNs with weights and activations constrained to {-1,+1} at run-time.

Neural networks have become ubiquitous in automatic speech recognition systems.

However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal.

In large part, rodents "see" the world through their whiskers, a powerful tactile