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Other variants of Boltzmann machines

(10th-Dec-2020) • Many other variants of Boltzmann machines are possible. Boltzmann machines may be extended with different training...

Undirected Models of Conditional Covariance

(8th-Dec-2020) • While the Gaussian RBM has been the canonical energy model for real-valued data, ( ) argue that the Gaussian RBM...

Boltzmann Machines for Real-Valued Data

(7th-Dec-2020) • While Boltzmann machines were originally developed for use with binary data, many applications such as image and audio...

Jointly Training Deep Boltzmann Machines

(6th-Dec-2020) • Classic DBMs require greedy unsupervised pretraining, and to perform classification well, require a separate MLP-based...

Layer-Wise Pretraining

(5th-Dce-2020) • Unfortunately, training a DBM using stochastic maximum likelihood (as described above) from a random initialization...

DBM Mean Field Inference

(4th-Dec-2020) • The conditional distribution over one DBM layer given the neighboring layers is factorial. In the example of the DBM...

Interesting Properties

(3rd-Dec-2020) • Deep Boltzmann machines have many interesting properties. DBMs were developed after DBNs. Compared to DBNs, the...

Deep Boltzmann Machines

(2nd-Dec-2020) • A deep Boltzmann machine or DBM (Salakhutdinov and Hinton 2009a , ) is another kind of deep, generative model. Unlike...

Conditional Distributions

(30th-Nov-2020) • Though P (v) is intractable, the bipartite graph structure of the RBM has the very special property that its...

Restricted Boltzmann Machines

(29th-Nov-2020) • Invented under the name harmonium ( , ), restricted Boltzmann Smolensky 1986 machines are some of the most common...

Deep Generative Models

(28th-Nov-2020) • Present several of the specific kinds of generative models that can be built and trained using the techniques presented...

Exploration Versus Exploitation

(26th-Nov-2020) When making recommendations to users, an issue arises that goes beyond ordinary supervised learning and into the realm of...

Historical Perspective

(25th-Nov-2020) • The idea of distributed representations for symbols was introduced by Rumelhart et al. ( ) in one of the first...

Neural Machine Translation

(23rd-Nov-2020) • Machine translation is the task of reading a sentence in one natural language and emitting a sentence with the...

Combining Neural Language Models with -grams n

(22nd-Nov-2020) • A major advantage of n-gram models over neural networks is that n-gram models achieve high model capacity (by storing...

Importance Sampling

(21st-Nov-2020) • One way to speed up the training of neural language models is to avoid explicitly computing the contribution of the...

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