Variational Autoencoder (VAE) - Revision history

BPeat at 02:25, 4 May 2020

2020-05-04T02:25:37Z

BPeat at 20:50, 26 April 2020

2020-04-26T20:50:52Z

BPeat at 21:46, 9 September 2019

2019-09-09T21:46:37Z

BPeat at 21:45, 9 September 2019

2019-09-09T21:45:38Z

BPeat at 00:42, 23 April 2019

2019-04-23T00:42:55Z

BPeat at 04:31, 3 February 2019

2019-02-03T04:31:14Z

BPeat at 10:48, 24 May 2018

2018-05-24T10:48:47Z

BPeat at 10:47, 24 May 2018

2018-05-24T10:47:06Z

BPeat at 03:05, 13 May 2018

2018-05-13T03:05:15Z

BPeat: BPeat moved page Variational Autoencoder to Variational Autoencoder (VAE) without leaving a redirect

2018-05-12T01:49:15Z

BPeat moved page Variational Autoencoder to Variational Autoencoder (VAE) without leaving a redirect

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 * [[Deep Belief Network (DBN)]]
 * [[Restricted Boltzmann Machine (RBM)]]
+* [http://dpkingma.com/sgvb_mnist_demo/demo.html Digit Fantasies by a Deep Generative Model | Durk Kingma]
 Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers.  Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).

← Older revision		Revision as of 20:50, 26 April 2020
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	* [[Clustering]]		* [[Clustering]]
	* [[Generative Tensorial Reinforcement Learning (GENTRL)]]		* [[Generative Tensorial Reinforcement Learning (GENTRL)]]
		+	* [[Deep Belief Network (DBN)]]
		+	* [[Restricted Boltzmann Machine (RBM)]]

@@ Line 12: / Line 12: @@
 * [[Clustering]]
 * [[Generative Tensorial Reinforcement Learning (GENTRL)]]
-* [[Variational Autoencoder (VAE)]]
 Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers.  Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).

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 * [[Autoencoder (AE) / Encoder-Decoder]]
 * [[Clustering]]
 Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers.  Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).

@@ Line 10: / Line 10: @@
 * [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
 * [[Autoencoder (AE) / Encoder-Decoder]]
 Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers.  Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).

← Older revision		Revision as of 04:31, 3 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=Variational+Autoencoders YouTube search...]		[http://www.youtube.com/results?search_query=Variational+Autoencoders YouTube search...]
		+	[http://www.google.com/search?q=Variational+Autoencoders+machine+learning+ML+artificial+intelligence ...Google search]

	* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo \| Fjodor Van Veen]		* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo \| Fjodor Van Veen]

← Older revision		Revision as of 10:48, 24 May 2018
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	* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo \| Fjodor Van Veen]		* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo \| Fjodor Van Veen]
		+	* [[Autoencoder (AE) / Encoder-Decoder]]

	Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers. Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).		Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers. Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).

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 http://www.asimovinstitute.org/wp-content/uploads/2016/09/vae.png
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← Older revision		Revision as of 03:05, 13 May 2018
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	Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers. Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).		Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers. Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).
		+
		+	http://www.asimovinstitute.org/wp-content/uploads/2016/09/vae.png

	<youtube>iz-TZOEKXzA</youtube>		<youtube>iz-TZOEKXzA</youtube>

← Older revision	Revision as of 01:49, 12 May 2018
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