[PDF] Top 20 Low-cost representation for restricted Boltzmann machines
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Low-cost representation for restricted Boltzmann machines
... a low-cost representation from restricted Boltzmann machines, which may be seen as a step towards the integration of deep networks in memory limited ...new representation ... See full document
10
Representation decomposition for knowledge extraction and sharing using restricted Boltzmann machines
... the low-cost confidence rules can be almost identical to that of the RBMs, with high ...the low-cost representation from the extracted rules with the feature extraction still o ff ering ... See full document
179
Voice conversion using speaker-dependent conditional restricted Boltzmann machine
... conditional restricted Boltzmann machines (CRBMs) for each speaker to obtain high-order speaker-independent spaces where voice features are converted more easily than those in an original acoustic ... See full document
12
Rate-coded Restricted Boltzmann Machines for Face Recognition
... Face recognition is difficult when the number of individuals is large and the test and training images of an individual differ in expression, pose, lighting or the date on which they were taken. In addition to being an ... See full document
7
Factored four way conditional restricted Boltzmann machines for activity recognition
... of low-power, low-bandwidth sensors, such as Passive InfraRed (PIR) sensors, and power and pressure meters placed either around the home, or on-body ... See full document
10
Deep-FS: a feature selection algorithm for deep Boltzmann machines
... Feature selection methods for classification can be divided into three main categories: filter, wrapper and embedded methods. These approaches are used to combine feature selection with a classification model. In filter ... See full document
16
Sequence Classification Restricted Boltzmann Machines With Gated Units
... rolling Restricted Boltzmann Machines with class labels over ...performs representation learning and inference efficiently and it is very compact with a number of parameters equivalent to that ... See full document
11
Initializing Neural Networks Using Restricted Boltzmann Machines
... Maximum likelihood (ML) is a method used to solve for the parameters of an assumed likelihood PDF, p(v | θ), so that a set of data is described by the model as best as possible [Theodoridis and Koutroumbas, 2008]. This ... See full document
133
Sentiment Aspect Extraction based on Restricted Boltzmann Machines
... Compared to standard RBMs, a crucial differ- ence is that hidden units now have a heterogeneous structure instead of being homogeneous as in the standard basic RBM model. In particular, we rely on three types of hidden ... See full document
10
Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition
... relation between two images in video sequence, approximate the joint distribution of motion-difference is more feasible. It is also worth noting that the features learned from Gaussian RBMs are low-level features. ... See full document
8
Hardware Acceleration on Cloud Services: The use of Restricted Boltzmann Machines on Handwritten Digits Recognition
... (i) Phase 1: Each image consists of a set of pixels, which are represented as binary values. This simplifies the multiplication part of (11), since instead of arithmetic multiplications, low complexity ... See full document
13
Improving sampling, optimization and feature extraction in Boltzmann machines
... of Restricted Boltzmann Machines (a type of MRF) to track the partition function during ...computational cost similar to that required for training ... See full document
207
Geometry and Expressive Power of Conditional Restricted Boltzmann Machines
... and finite Gibbs sampling (Hinton, 2002, 2012), even when they are defined with many units and parameters. An RBM defines Gibbs-Boltzmann probability distributions over the observable states of the network, ... See full document
32
Article Imposing Higher-Level Structure in Polyphonic Music Generation Using Convolutional Restricted Boltzmann Machines and Constraints
... As mentioned above, the illustrated samples are the best four of 20 with respect to the overall cost. The most obvious shortcoming of samples not selected because of higher cost is that they do not satisfy ... See full document
31
Restricted Boltzmann Machine vectors for speaker clustering
... like Restricted Boltzmann Machines (RBMs), Deep Belief Net- works (DBNs) and Deep Autoencoders have the ability of rep- resentational learning ...speaker representation in the form of speaker ... See full document
5
How to Center Deep Boltzmann Machines
... rather low for all models since the difference between the highest and the final LL values is rather small, indicating that no divergence or overfitting has happened so ...current representation as ... See full document
61
Learning Algorithms for the Classification Restricted Boltzmann Machine
... Classification Restricted Boltzmann Machine (ClassRBM), the proposed approach and learning algorithms address both aforementioned ...computational cost per example of ... See full document
27
Temporal Restricted Boltzmann Machines for Dependency Parsing
... We analyzed the latent layers in our models to see if they captured semantic patterns. A latent layer is a vector of 100 latent variables. Every Shift operation gives a latent representation for the corresponding ... See full document
7
Deep Learning using Restricted Boltzmann Machines
... ) Boltzmann Machines (BM) is the form of log- linear Markov Random Field (MRF), where the energy function is linear in its free parameters ...the Boltzmann Machine. Restricted Boltzmann ... See full document
5
Improved learning algorithms for restricted Boltzmann machines
... data representation, for example, a bit-flipping transformation for RBM with both binary visible and hidden neurons, and the sparsity of the data set does not affect learning ... See full document
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