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Graphical models
From:
VideoLectures
on
Wed, Oct 13 2010 8:57 PM
This course covers the basics of Probabilistic Graphical Models, including the basic theory of Bayesian Networks and Markov Random Fields, as well as inference and learning algorithms and applications.
Graphical Models, Variational Methods, and Message-Passing
From:
VideoLectures
on
Wed, Oct 13 2010 7:35 PM
Inference in Graphical Models
From:
VideoLectures
on
Wed, Oct 13 2010 6:53 PM
This short course will cover the basics of inference in graphical models. It will start by explaining the theory of probabilistic graphical models, including concepts of conditional independence and factorisation and how they arise in both Markov random fields and Bayesian Networks. He will ...
Graphical Models for Speech Recognition: Articulatory and Audio-Visual Models
From:
VideoLectures
on
Wed, Oct 13 2010 6:33 PM
Since the 1980s, the main approach to automatic speech recognition has been using hidden Markov models (HMMs), in which each state corresponds to a phoneme or part of a phoneme in the context of the neighboring phonemes. Despite their crude approximation of the speech signal, and the large margi...
1 Billion Instances, 1 Thousand Machines and 3.5 Hours
From:
VideoLectures
on
Wed, Oct 13 2010 6:13 PM
Training conditional maximum entropy models on massive data sets requires significant computational resources, but by distributing the computation, training time can be significant reduced. Recent theoretical results have demonstrated conditional maximum entropy models trained by weight mixtures...
Graphical Models
From:
VideoLectures
on
Wed, Oct 13 2010 5:55 PM
In the last decade probabilistic graphical models — in particular Bayes networks and Markov networks -- became very popular as tools for structuring uncertain knowledge about a domain of interest and for building knowledge-based systems that allow sound and efficient inferences about this...
Probabilistic Graphical Models and Structured Prediction
From:
VideoLectures
on
Wed, Oct 13 2010 5:13 PM
Procedural Modeling of Architectures: Towards Large Scale Visual Reconstruction
From:
VideoLectures
on
Wed, Oct 13 2010 4:28 PM
Three-dimensional content is a novel modality used in numerous domains like navigation, post production & cinematography, architectural modeling and urban planning. These domains have benefited from the enormous progress has been made on 3D reconstruction from images. Such a problem consists of ...
Trees for Regression and Classification
From:
VideoLectures
on
Tue, Oct 12 2010 4:08 PM
Tree models are widely used for regression and classification problems, with interpretability and ease of implementation being among their chief attributes. Despite the widespread use tree models, a comprehensive theoretical analysis of their performance has only begun to emerge in recent years....
Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis
From:
VideoLectures
on
Tue, Oct 12 2010 4:01 PM
In this paper, we explore an application of basis pursuit to audio scene analysis. The goal of our work is to detect when certain sounds are present in a mixed audio signal. We focus on the regime where out of a large number of possible sources, a small but unknown number combine and overlap t...
Reading Tea Leaves: How Humans Interpret Topic Models
From:
VideoLectures
on
Mon, Oct 11 2010 5:36 PM
Probabilistic topic models are a commonly used tool for analyzing text data, where the latent topic representation is used to perform qualitative evaluation of models and guide corpus exploration. Practitioners typically assume that the latent space is semantically meaningful, but this important...
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