Generative Modelling Language
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Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this… Expand Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks… Expand Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In… Expand Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to… Expand This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial… Expand We study the problem of learning generative models of 3D shapes. Voxels or 3D parts have been widely used as the underlying… Expand We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays… Expand We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object recognition task. The top-level… Expand This paper describes a computer vision based system for real-time robust traffic sign detection, tracking, and recognition. Such… Expand Many difficult visual perception problems, like 3D human motion estimation, can be formulated in terms of inference using complex… Expand