Ruslan Salakhutdinov OER/OCW Courses

Bayesian Probabilistic Matrix Factorization by Ruslan @VideoLectures

Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP... Watch Video

Learning Feature Hierarchies by Learning Ruslan Salakhutdinov @VideoLectures

In this paper we present several ideas based on learning deep generative models from high-dimensional, richly structured sensory input. We will exploit the following two key properties... Watch Video

Learning Nonlinear Dynamic Models by Ruslan Salakhutdinov @VideoLectures

We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise difficult. We provide theory... Watch Video

Multimodal Learning with Deep Boltzmann by Ruslan @VideoLectures

We propose a Deep Boltzmann Machine for learning a generative model of multimodal data. We show how to use the model to extract a meaningful representation of multimodal data. We... Watch Video

On the Quantitative Analysis of Deep Belief by Ruslan @VideoLectures

Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allowed these... Watch Video

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