Machine Learning

Neural Universal Discrete Denoiser

Abstract : In this talk, I will present a novel framework of applying deep neural network (DNN) to discrete denoising problem. DNN has recently shown remarkable performance improvements in diverse applications, and most of the success are based on the supervised […]

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Deep Image Restoration

Abstract : Image restoration or deconvolution that is to recover the original clean image from a noisy or corrupted image has long been an important and fundamental problem in image processing and computer vision. In this talk, a very effective deep […]

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Bayesian Deep Probabilistic Models

Abstract : Deep generative models are a rich class of models for density estimation which specify a generative process for observed data using a set of stochastic latent variables. Among those, of particular interest is the variational autoencoder (VAE) which is […]

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Quickly Analyzing Sensitivity of Incremental Data Update for Big Data Machine Learning in Changing Environment

Abstract : In this talk, we are concerned with large-scale machine learning problems in changing environment where a small part of the dataset which have been used for training a machine learning model is incrementally updated, and the effect of the […]

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Understanding Deep Learning and Neural Semantics

Abstract : Deep learning has achieved great success in computer vision. Many people believe that the success is due to employing a huge number of parameters to fit big training data. In this talk, I will show that neuron responses of […]

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