DR.GEEK
Dec 10, 20202 min read
Other variants of Boltzmann machines
(10th-Dec-2020) • Many other variants of Boltzmann machines are possible. Boltzmann machines may be extended with different training...
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DR.GEEK
Dec 9, 20202 min read
Boltzmann Machines for Structured or Sequential Outputs
(9th-Dec-2020) • In the structured output scenario, we wish to train a model that can map from some input x to some output y, and the...
DR.GEEK
Dec 8, 20202 min read
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...
DR.GEEK
Dec 7, 20201 min read
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...
DR.GEEK
Dec 6, 20201 min read
Jointly Training Deep Boltzmann Machines
(6th-Dec-2020) • Classic DBMs require greedy unsupervised pretraining, and to perform classification well, require a separate MLP-based...
DR.GEEK
Dec 5, 20201 min read
Layer-Wise Pretraining
(5th-Dce-2020) • Unfortunately, training a DBM using stochastic maximum likelihood (as described above) from a random initialization...
DR.GEEK
Dec 4, 20202 min read
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...
DR.GEEK
Dec 3, 20202 min read
Interesting Properties
(3rd-Dec-2020) • Deep Boltzmann machines have many interesting properties. DBMs were developed after DBNs. Compared to DBNs, the...
DR.GEEK
Dec 2, 20201 min read
Deep Boltzmann Machines
(2nd-Dec-2020) • A deep Boltzmann machine or DBM (Salakhutdinov and Hinton 2009a , ) is another kind of deep, generative model. Unlike...
DR.GEEK
Dec 1, 20201 min read
Training Restricted Boltzmann Machines&Deep belief networks (DBNs)
(1st-Dec-2020) • Because the RBM admits efficient evaluation and differentiation of ˜ P(v) and efficient MCMC sampling in the form of block...
DR.GEEK
Nov 30, 20201 min read
Conditional Distributions
(30th-Nov-2020) • Though P (v) is intractable, the bipartite graph structure of the RBM has the very special property that its...
DR.GEEK
Nov 29, 20201 min read
Restricted Boltzmann Machines
(29th-Nov-2020) • Invented under the name harmonium ( , ), restricted Boltzmann Smolensky 1986 machines are some of the most common...
DR.GEEK
Nov 28, 20201 min read
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...
DR.GEEK
Nov 27, 20202 min read
Knowledge Representation, Reasoning and Question Answering
(27th-Nov-2020) • Deep learning approaches have been very successful in language modeling, machine translation and natural language...
DR.GEEK
Nov 26, 20202 min read
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...
DR.GEEK
Nov 25, 20203 min read
Historical Perspective
(25th-Nov-2020) • The idea of distributed representations for symbols was introduced by Rumelhart et al. ( ) in one of the first...
DR.GEEK
Nov 24, 20201 min read
Using an Attention Mechanism and Aligning Pieces of Data
(24th-Nov-2020) • We can think of an attention-based system as having three components: • 1. A process that “reads” raw data (such as...
DR.GEEK
Nov 23, 20201 min read
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...
DR.GEEK
Nov 22, 20201 min read
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...
DR.GEEK
Nov 21, 20201 min read
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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