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  1. Boltzmann Machine was invented by Geoffrey Hinton and Terry Sejnowski in 1985.[1]
  2. The main purpose of Boltzmann Machine is to optimize the solution of a problem.[1]
  3. The following diagram shows the architecture of Boltzmann machine.[1]
  4. That is, there is no intra-layer communication – this is the restriction in a restricted Boltzmann machine.[2]
  5. Each time contrastive divergence is run, it’s a sample of the Markov Chain composing the restricted Boltzmann machine.[2]
  6. A continuous restricted Boltzmann machine is a form of RBM that accepts continuous input (i.e. numbers cut finer than integers) via a different type of contrastive divergence sampling.[2]
  7. An effective continuous restricted Boltzmann machine employs a Gaussian transformation on the visible (or input) layer and a rectified-linear-unit transformation on the hidden layer.[2]
  8. X Restricted Boltzmann Machine (RBM) is an essential component in many machine learning applications.[3]
  9. Generalized Boltzmann Machine with Deep Neural Structure.[3]
  10. Boltzmann Machine were first invented in 1985 by Geoffrey Hinton, a professor at the University of Toronto.[4]
  11. Boltzmann Machine doesn’t expect input data, it generate data.[4]
  12. For Boltzmann Machine all neurons are same, it doesn’t discriminate between hidden and visible neurons.[4]
  13. The way this system work, we use our training data and feed into the Boltzmann Machine as input to help system adjust its weights.[4]
  14. Even prior to it, Hinton along with Terry Sejnowski in 1985 invented an Unsupervised Deep Learning model, named Boltzmann Machine.[5]
  15. There is also another type of Boltzmann Machine, known as Deep Boltzmann Machines (DBM).[5]
  16. The visible units of Restricted Boltzmann Machine can be multinomial, although the hidden units are Bernoulli.[6]
  17. A graphical representation of a Boltzmann machine with a few weights labeled.[7]
  18. A Boltzmann machine, like a Hopfield network, is a network of units with an "energy" (Hamiltonian) defined for the overall network.[7]
  19. The distribution over global states converges as the Boltzmann machine reaches thermal equilibrium.[7]
  20. Boltzmann machine training involves two alternating phases.[7]
  21. To reduce this effect, a restricted Boltzmann machine (RBM) can be used.[8]
  22. Because Boltzmann machine weight updates only require looking at the expected distributions of surrounding neurons, it is a plausible model for how actual biological neural networks learn.[8]
  23. Yes, Restricted Boltzmann Machine (RBM) CAN be used to initiate the weights of a neural network.[9]
  24. Here, we introduce a representation of the wave-function coefficients in terms of a deep Boltzmann machine (DBM)37.[10]
  25. Restricted Boltzmann machine is the most important part of the deep belief network.[11]
  26. It is a type of Boltzmann machine with no link between any visible nodes or hidden nodes.[11]
  27. The structural characteristics of the restricted Boltzmann machine indicate that the hidden units and the visual ones are respectively independent.[11]
  28. That is why architectures of neural networks that we are going to examine in this article, (Boltzmann machine), have a different approach.[12]
  29. To understand the Restricted Boltzmann Machine, we need to understand the standard Boltzmann Machine first.[12]
  30. The Boltzmann Machine is one type of Energy-Based Models.[12]
  31. The inputs’ value doesn’t need to have values for every neuron; the Boltzmann machine will generate it for us.[12]
  32. Restricted Boltzmann Machine is an undirected graphical model that plays a major role in Deep Learning Framework in recent times.[13]
  33. Now with this, we come to an end to this Restricted Boltzmann Machine Tutorial.[13]
  34. So, if you have read this, you are no longer a newbie to Restricted Boltzmann Machine.[13]
  35. Definition - What does Boltzmann Machine mean?[14]
  36. They are a special class of Boltzmann Machine in that they have a restricted number of connections between visible and hidden units.[15]
  37. Furthermore, analog matrix multiplication based on the memristor crossbar has been shown as significantly superior to the digital version for the Boltzmann machine.[16]
  38. This article presents an efficient hardware architecture of restricted Boltzmann machine (RBM) that is an important category of NN systems.[17]
  39. There are two types of nodes in the Boltzmann Machine — Visible nodes — those nodes which we can and do measure, and the Hidden nodes – those nodes which we cannot or do not measure.[18]
  40. Although the node types are different, the Boltzmann machine considers them as the same and everything works as one single system.[18]
  41. The training data is fed into the Boltzmann Machine and the weights of the system are adjusted accordingly.[18]
  42. Boltzmann Distribution is used in the sampling distribution of the Boltzmann Machine.[18]

소스

  1. ↑ 1.0 1.1 1.2 Boltzmann Machine
  2. ↑ 2.0 2.1 2.2 2.3 A Beginner's Guide to Restricted Boltzmann Machines (RBMs)
  3. ↑ 3.0 3.1 Generalized Boltzmann Machine with Deep Neural Structure
  4. ↑ 4.0 4.1 4.2 4.3 An Intuitive Introduction Of Boltzmann Machine
  5. ↑ 5.0 5.1 Boltzmann Machines | Transformation of Unsupervised Deep Learning — Part 1
  6. ↑ Restricted Boltzmann machine
  7. ↑ 7.0 7.1 7.2 7.3 Boltzmann machine
  8. ↑ 8.0 8.1 Artificial Neural Networks/Boltzmann Machines
  9. ↑ Restricted Boltzmann Machine : how is it used in machine learning?
  10. ↑ Constructing exact representations of quantum many-body systems with deep neural networks
  11. ↑ 11.0 11.1 11.2 Three-dimensional convolutional restricted Boltzmann machine for human behavior recognition from RGB-D video
  12. ↑ 12.0 12.1 12.2 12.3 Introduction to Restricted Boltzmann Machines
  13. ↑ 13.0 13.1 13.2 Restricted Boltzmann Machine Tutorial
  14. ↑ What is a Boltzmann Machine?
  15. ↑ Restricted Boltzmann Machine Explained
  16. ↑ PrxCa1−xMnO3 based stochastic neuron for Boltzmann machine to solve “maximum cut” problem
  17. ↑ VLSI Architectures for the Restricted Boltzmann Machine
  18. ↑ 18.0 18.1 18.2 18.3 Types of Boltzmann Machines

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  • [{'LOWER': 'boltzmann'}, {'LEMMA': 'machine'}]
  • [{'LOWER': 'stochastic'}, {'LOWER': 'hopfield'}, {'LOWER': 'network'}, {'LOWER': 'with'}, {'LOWER': 'hidden'}, {'LEMMA': 'unit'}]
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