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Representation learning: A review and new perspectives
The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …
we hypothesize that this is because different representations can entangle and hide more or …
[CITACE][C] Unsupervised feature learning and deep learning: A review and new perspectives
The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …
we hypothesize that this is because different representations can entangle and hide more or …
BERT has a mouth, and it must speak: BERT as a Markov random field language model
We show that BERT (Devlin et al., 2018) is a Markov random field language model. This
formulation gives way to a natural procedure to sample sentences from BERT. We generate …
formulation gives way to a natural procedure to sample sentences from BERT. We generate …
[KNIHA][B] Deep learning
Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their
careers in deep learning and artificial intelligence research. The other target audience …
careers in deep learning and artificial intelligence research. The other target audience …
Neural autoregressive distribution estimation
We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural
network architectures applied to the problem of unsupervised distribution and density …
network architectures applied to the problem of unsupervised distribution and density …
Deep learning of representations: Looking forward
Y Bengio - International conference on statistical language and …, 2013 - Springer
Deep learning research aims at discovering learning algorithms that discover multiple levels
of distributed representations, with higher levels representing more abstract concepts …
of distributed representations, with higher levels representing more abstract concepts …
An introduction to restricted Boltzmann machines
Abstract Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can
be interpreted as stochastic neural networks. The increase in computational power and the …
be interpreted as stochastic neural networks. The increase in computational power and the …
Training restricted Boltzmann machines: An introduction
Abstract Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can
be interpreted as stochastic neural networks. They have attracted much attention as building …
be interpreted as stochastic neural networks. They have attracted much attention as building …
An overview on restricted Boltzmann machines
Abstract The Restricted Boltzmann Machine (RBM) has aroused wide interest in machine
learning fields during the past decade. This review aims to report the recent developments in …
learning fields during the past decade. This review aims to report the recent developments in …
Better mixing via deep representations
It has been hypothesized, and supported with experimental evidence, that deeper
representations, when well trained, tend to do a better job at disentangling the underlying …
representations, when well trained, tend to do a better job at disentangling the underlying …