- Week 8 – Lecture: Contrastive methods and regularised latent variable models

Week 8 – Lecture: Contrastive methods and regularised latent variable models

Course website: http://bit.ly/DLSP20-web
Playlist: http://bit.ly/pDL-YouTube
Speaker: Yann LeCun
Week 8: http://bit.ly/DLSP20-08

0:00:00 – Week 8 – Lecture

LECTURE Part A: http://bit.ly/DLSP20-08-1
In this section, we focused on the introduction of contrastive methods in Energy-Based Models in ...
Course website: http://bit.ly/DLSP20-web
Playlist: http://bit.ly/pDL-YouTube
Speaker: Yann LeCun
Week 8: http://bit.ly/DLSP20-08

0:00:00 – Week 8 – Lecture

LECTURE Part A: http://bit.ly/DLSP20-08-1
In this section, we focused on the introduction of contrastive methods in Energy-Based Models in several aspects. First, we discuss the advantage brought by applying contrastive methods in self-supervised learning. Second, we discussed the architecture of denoising autoencoders and their weakness in image reconstruction tasks. We also talked about other contrastive methods, like contrastive divergence and persistent contrastive divergence.
0:00:05 – Recap on EBM and Characteristics of Different Contrastive Methods
0:10:13 – Contrastive Methods in Self-Supervised Learning
0:23:04 – Denoising Autoencoder and other Contrastive methods

LECTURE Part B: http://bit.ly/DLSP20-08-2
In this section, we discussed regularized latent variable EBMs in detail covering concepts of conditional and unconditional versions of these models. We then discussed the algorithms of ISTA, FISTA and LISTA and look at examples of sparse coding and filters learned from convolutional sparse encoders. Finally we talked about Variational Auto-Encoders and the underlying concepts involved.
0:37:13 – Overview of Regularized Latent Variable Energy Based Models and Sparse Coding
1:07:46 – Convolutional Sparse Auto-Encoders
1:12:51 – Variational Auto-Encoders

#Yann LeCun #Deep Learning #PyTorch #NYU #EBM #Energy Based Models #SSL #Semi Supervised Learning #LV #Latent Variable #contrastive methods #Regularised Latent Variables
– Week 8 – Lecture - Week 8 – Lecture: Contrastive methods and regularised latent variable models

– Week 8 – Lecture

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
00:00:00 - 00:00:05
– Recap on EBM and Characteristics of Different Contrastive Methods - Week 8 – Lecture: Contrastive methods and regularised latent variable models

– Recap on EBM and Characteristics of Different Contrastive Methods

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
00:00:05 - 00:10:13
– Contrastive Methods in Self-Supervised Learning - Week 8 – Lecture: Contrastive methods and regularised latent variable models

– Contrastive Methods in Self-Supervised Learning

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
00:10:13 - 00:23:04
– Denoising Autoencoder and other Contrastive methods - Week 8 – Lecture: Contrastive methods and regularised latent variable models

– Denoising Autoencoder and other Contrastive methods

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
00:23:04 - 00:37:13
– Overview of Regularized Latent Variable Energy Based Models and Sparse Coding - Week 8 – Lecture: Contrastive methods and regularised latent variable models

– Overview of Regularized Latent Variable Energy Based Models and Sparse Coding

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
00:37:13 - 01:07:46
At  I don't understand how z and z_bar can be made similar using the D function. Are they distributions and you're applying KL divergence?I mean if z is a latent variable, how is it's value know for comparison? - Week 8 – Lecture: Contrastive methods and regularised latent variable models

At I don't understand how z and z_bar can be made similar using the D function. Are they distributions and you're applying KL divergence?I mean if z is a latent variable, how is it's value know for comparison?

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
01:03:00 - 01:39:26
– Convolutional Sparse Auto-Encoders - Week 8 – Lecture: Contrastive methods and regularised latent variable models

– Convolutional Sparse Auto-Encoders

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
01:07:46 - 01:12:51
– Variational Auto-Encoders - Week 8 – Lecture: Contrastive methods and regularised latent variable models

– Variational Auto-Encoders

Week 8 – Lecture: Contrastive methods and regularised latent variable models
2020年05月19日 
01:12:51 - 01:39:26

Alfredo Canziani

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