Neurobayesian models 2019 - История изменений

Tipt0p: /* Exam */

2019-04-15T11:23:02Z

Exam

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	===Exam ===		===Exam ===
	~~TBA~~		Exam questions are published [https://goo.gl/hNjh5M here].

			At the beginning of the exam, we will give you a random question from the main part of the exam questions list. You will have one hour to prepare your answer. During this hour you may use any materials (including the ones on your laptop).

			Then you discuss your answer with an examiner, answer questions from the theoretical minimum, answer additional questions on the course and solve problems. At this part of the exam, you CAN NOT use any materials. Please pay attention to the theoretical minimum - inability to answer any questions from it automatically entails an unsatisfactory mark for the exam.

	=== Course Plan ===		=== Course Plan ===

Tipt0p: /* News */

2019-03-10T21:14:51Z

News

← Предыдущая версия		Версия от 21:14, 10 марта 2019
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	* The second assignment has been uploaded to anytask. Deadline: 8 March, 23:00.		* The second assignment has been uploaded to anytask. Deadline: 8 March, 23:00.
	* The third assignment has been uploaded to anytask. Deadline: 15 March, 23:00.		* The third assignment has been uploaded to anytask. Deadline: 15 March, 23:00.
			* A hard deadline for all assignments: 20 March, 23:00.

	===Grading System===		===Grading System===

Tipt0p: /* Assignments */

2019-03-10T21:14:29Z

Assignments

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	Approximate dates of assignments' upload: 7 February, 14 February, 28 February		Approximate dates of assignments' upload: 7 February, 14 February, 28 February

	A hard deadline for all assignments: 23 March, 23:00.		A hard deadline for all assignments: 20 March, 23:00.

	===Exam ===		===Exam ===

Tipt0p: /* Assignments */

2019-03-10T21:14:22Z

Assignments

← Предыдущая версия		Версия от 21:14, 10 марта 2019
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	Approximate dates of assignments' upload: 7 February, 14 February, 28 February		Approximate dates of assignments' upload: 7 February, 14 February, 28 February

	~~At the end of the module before the exam there will be a~~ hard deadline for all assignments~~! Exact date will be announced later~~.		A hard deadline for all assignments: 23 March, 23:00.

	===Exam ===		===Exam ===

Tipt0p в 16:24, 6 марта 2019

2019-03-06T16:24:53Z

← Предыдущая версия		Версия от 16:24, 6 марта 2019
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	\| 28 February \|\| Seminar: Local reparametrization trick		\| 28 February \|\| Seminar: Local reparametrization trick
	\|-		\|-
	~~\| rowspan="2"~~ \| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks		\| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks
	\|-		\|-
	\| 7 March \|\| Lecture: Discrete Latent Variables and Variance Reduction		\| rowspan="2" \| 7 \|\| 7 March \|\| Lecture: Discrete Latent Variables and Variance Reduction
	\|-		\|-
	~~\| rowspan="2" \| 7 \|~~\| 7 March \|\| Seminar: Discrete Latent Variables and Variance Reduction		\| 7 March \|\| Seminar: Discrete Latent Variables and Variance Reduction
	\|-		\|-
	\| 14 March \|\| Seminar: Deep Markov chain Monte Carlo (MCMC)		\| 8\|\|14 March \|\| Seminar: Deep Markov chain Monte Carlo (MCMC)
	\|-		\|-
	\| rowspan="2" \| 8 \|\| 14 March \|\| Lecture: Semi-implicit variational inference		\| rowspan="2" \| 9 \|\| 14 March \|\| Lecture: Semi-implicit variational inference
	\|-		\|-
	\| 21 March \|\| Seminar: VampPrior		\| 21 March \|\| Seminar: VampPrior

Tipt0p: /* Course Plan */

2019-03-06T16:23:02Z

Course Plan

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	\| rowspan="2" \| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks		\| rowspan="2" \| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks
	\|-		\|-
	\| 7 March \|\| ~~Seminar~~: ~~Deep Markov chain Monte Carlo (MCMC)~~		\| 7 March \|\| Lecture: Discrete Latent Variables and Variance Reduction
	\|-		\|-
	\| rowspan="2" \| 7 \|\| 7 March \|\| ~~Lecture~~: Discrete Latent Variables and Variance Reduction		\| rowspan="2" \| 7 \|\| 7 March \|\| Seminar: Discrete Latent Variables and Variance Reduction
	\|-		\|-
	\| 14 March \|\| Seminar: ~~Discrete Latent Variables and Variance Reduction~~		\| 14 March \|\| Seminar: Deep Markov chain Monte Carlo (MCMC)
	\|-		\|-
	\| rowspan="2" \| 8 \|\| 14 March \|\| Lecture: Semi-implicit variational inference		\| rowspan="2" \| 8 \|\| 14 March \|\| Lecture: Semi-implicit variational inference

Tipt0p: /* News */

2019-03-01T12:09:50Z

News

← Предыдущая версия		Версия от 12:09, 1 марта 2019
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	* The first assignment has been uploaded to anytask. Deadline: 22 February, 23:00.		* The first assignment has been uploaded to anytask. Deadline: 22 February, 23:00.
	* The second assignment has been uploaded to anytask. Deadline: 8 March, 23:00.		* The second assignment has been uploaded to anytask. Deadline: 8 March, 23:00.
			* The third assignment has been uploaded to anytask. Deadline: 15 March, 23:00.

	===Grading System===		===Grading System===

Tipt0p: /* News */

2019-02-15T13:32:30Z

News

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	=== News ===		=== News ===
	* The first assignment has been uploaded to anytask. Deadline: 22 February, 23:00.		* The first assignment has been uploaded to anytask. Deadline: 22 February, 23:00.
			* The second assignment has been uploaded to anytask. Deadline: 8 March, 23:00.

	===Grading System===		===Grading System===

Tipt0p: /* Course Plan */

2019-02-13T10:52:27Z

Course Plan

← Предыдущая версия		Версия от 10:52, 13 февраля 2019
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	\| rowspan="2" \| 5 \|\| 21 February \|\| Lecture: Bayesian neural networks		\| rowspan="2" \| 5 \|\| 21 February \|\| Lecture: Bayesian neural networks
	\|-		\|-
	\| 28 February \|\| Seminar: Local reparametrization trick \|\|		\| 28 February \|\| Seminar: Local reparametrization trick
	\|-		\|-
	\| rowspan="2" \| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks		\| rowspan="2" \| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks

Tipt0p: /* Course Plan */

2019-02-13T10:52:16Z

Course Plan

← Предыдущая версия		Версия от 10:52, 13 февраля 2019
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	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-
	! '''№''' !! '''Date''' !! '''Theme~~''' !! '''Materials~~'''		! '''№''' !! '''Date''' !! '''Theme'''
	\|-		\|-
	\| rowspan="2" \| 1 \|\| 24 January \|\| Lecture: Stochastic Variational Inference ~~\|\| rowspan="2" \| [http://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf article]~~		\| rowspan="2" \| 1 \|\| 24 January \|\| Lecture: Stochastic Variational Inference
	\|-		\|-
	\|31 January\|\|Seminar: Application of SVI to Latent Dirichlet Allocation model \|\|		\|31 January\|\|Seminar: Application of SVI to Latent Dirichlet Allocation model
	\|-		\|-
	\| rowspan="2" \| 2 \|\| 31 January \|\| Lecture: Doubly Stochastic Variational Inference ~~\|\| [http://proceedings.mlr.press/v32/titsias14.pdf article]~~		\| rowspan="2" \| 2 \|\| 31 January \|\| Lecture: Doubly Stochastic Variational Inference
	\|-		\|-
	\| 7 February \|\| Seminar: Doubly Stochastic Variational Inference ~~\|\| [http://www.argmin.net/2018/02/20/reinforce/ blogpost], [https://arxiv.org/abs/1502.03044 article]~~		\| 7 February \|\| Seminar: Doubly Stochastic Variational Inference
	\|-		\|-
	\| rowspan="2" \| 3 \|\| 7 February \|\| Lecture: Variational autoencoders (VAE) and normalizing flows (NF) ~~\|\| [https://arxiv.org/abs/1312.6114 article], [https://arxiv.org/abs/1505.05770 article],~~		\| rowspan="2" \| 3 \|\| 7 February \|\| Lecture: Variational autoencoders (VAE) and normalizing flows (NF)
	\|-		\|-
	\| 14 February \|\| Seminar: Importance Weighted Autoencoders + more complex NF ~~\|\| [https://arxiv.org/abs/1509.00519 article]~~		\| 14 February \|\| Seminar: Importance Weighted Autoencoders + more complex NF
	\|-		\|-
	\| rowspan="2" \| 4 \|\| 14 February \|\| Lecture: Implicit Variational Inference using Adversarial Training ~~\|\| [https://arxiv.org/abs/1701.04722 article]~~		\| rowspan="2" \| 4 \|\| 14 February \|\| Lecture: Implicit Variational Inference using Adversarial Training
	\|-		\|-
	\| 21 February \|\| Seminar: f-GAN ~~\|\| [https://arxiv.org/abs/1606.00709 article]~~		\| 21 February \|\| Seminar: f-GAN
	\|-		\|-
	\| rowspan="2" \| 5 \|\| 21 February \|\| Lecture: Bayesian neural networks ~~\|\| [https://arxiv.org/abs/1505.05424 article], [http://proceedings.mlr.press/v28/wang13a.pdf article], [https://arxiv.org/abs/1703.01961 article ]~~		\| rowspan="2" \| 5 \|\| 21 February \|\| Lecture: Bayesian neural networks
	\|-		\|-
	\| 28 February \|\| Seminar: Local reparametrization trick \|\| ~~[https://arxiv.org/abs/1506.02557 article]~~		\| 28 February \|\| Seminar: Local reparametrization trick \|\|
	\|-		\|-
	\| rowspan="2" \| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks ~~\|\| [https://arxiv.org/abs/1701.05369 article], [https://arxiv.org/abs/1702.04008 article]~~		\| rowspan="2" \| 6 \|\| 28 February \|\| Lecture: Bayesian compression of neural networks
	\|-		\|-
	\| 7 March \|\| Seminar: Deep Markov chain Monte Carlo (MCMC)~~\|\| [https://arxiv.org/abs/1706.07561 article] [https://arxiv.org/abs/1711.09268 article]~~		\| 7 March \|\| Seminar: Deep Markov chain Monte Carlo (MCMC)
	\|-		\|-
	\| rowspan="2" \| 7 \|\| 7 March \|\| Lecture: Discrete Latent Variables and Variance Reduction ~~\|\| rowspan="2" \| [https://arxiv.org/abs/1611.01144 article] [https://arxiv.org/abs/1611.00712 article] [https://arxiv.org/abs/1711.00123 article]~~		\| rowspan="2" \| 7 \|\| 7 March \|\| Lecture: Discrete Latent Variables and Variance Reduction
	\|-		\|-
	\| 14 March \|\| Seminar: Discrete Latent Variables and Variance Reduction		\| 14 March \|\| Seminar: Discrete Latent Variables and Variance Reduction
	\|\|
	\|-		\|-
	\| rowspan="2" \| 8 \|\| 14 March \|\| Lecture: Semi-implicit variational inference ~~\|\| [https://arxiv.org/abs/1805.11183 article], [https://arxiv.org/abs/1810.02789 article]~~		\| rowspan="2" \| 8 \|\| 14 March \|\| Lecture: Semi-implicit variational inference
	\|-		\|-
	\| 21 March \|\| Seminar: VampPrior ~~\|\| [https://arxiv.org/abs/1705.07120 article], [https://arxiv.org/abs/1809.05284 article]~~		\| 21 March \|\| Seminar: VampPrior
	\|}		\|}

			=== Course Materials ===
			[https://docs.google.com/document/d/1Mqzo89OeX5gteQ9qYF5rhz6Ap00JQEyoiq2WKo54vA8/edit?usp=sharing List of all materials (relevant papers, blogposts, etc.)]

	====Reading List ====		====Reading List ====