– XLSR: multilingual speech recognition(00:27:14 - 00:29:16) - 06L – Latent variable EBMs for structured prediction

– XLSR: multilingual speech recognition(00:27:14 - 00:29:16)
06L – Latent variable EBMs for structured prediction

Course website: http://bit.ly/DLSP21-web
Playlist: http://bit.ly/DLSP21-YouTube
Speaker: Yann LeCun

Chapters
00:00:00 – Welcome to class
00:00:17 – Training of an EBM
00:04:27 – Contrastive vs. regularised / architectural methods
00:05:21 – General margin loss
00:09:34 – List of loss functions
0...
Course website: http://bit.ly/DLSP21-web
Playlist: http://bit.ly/DLSP21-YouTube
Speaker: Yann LeCun

Chapters
00:00:00 – Welcome to class
00:00:17 – Training of an EBM
00:04:27 – Contrastive vs. regularised / architectural methods
00:05:21 – General margin loss
00:09:34 – List of loss functions
00:13:45 – Generalised additive margin loss
00:17:53 – Joint embedding architectures
00:21:29 – Wav2Vec 2.0
00:27:14 – XLSR: multilingual speech recognition
00:29:16 – Generative adversarial networks (GANs)
00:37:24 – Mode collapse
00:41:45 – Non-contrastive methods
00:43:19 – BYOL: bootstrap your own latent
00:44:27 – SwAV
00:46:45 – Barlow twins
00:51:29 – SEER
00:54:29 – Latent variable models in practice
00:57:34 – DETR
01:01:21 – Structured prediction
01:04:53 – Factor graph
01:12:47 – Viterbi algorithm whiteboard time
01:30:24 – Graph transformer networks
01:46:48 – Graph composition, transducers
01:48:38 – Final remarks

#PyTorch #NYU #Yann LeCun #Deep Learning #neural networks
– Welcome to class - 06L – Latent variable EBMs for structured prediction

– Welcome to class

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:00:00 - 00:00:17
– Training of an EBM - 06L – Latent variable EBMs for structured prediction

– Training of an EBM

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:00:17 - 00:04:27
– Contrastive vs. regularised / architectural methods - 06L – Latent variable EBMs for structured prediction

– Contrastive vs. regularised / architectural methods

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:04:27 - 00:05:21
– General margin loss - 06L – Latent variable EBMs for structured prediction

– General margin loss

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:05:21 - 00:09:34
– List of loss functions - 06L – Latent variable EBMs for structured prediction

– List of loss functions

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:09:34 - 00:13:45
– Generalised additive margin loss - 06L – Latent variable EBMs for structured prediction

– Generalised additive margin loss

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:13:45 - 00:17:53
– Joint embedding architectures - 06L – Latent variable EBMs for structured prediction

– Joint embedding architectures

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:17:53 - 00:21:29
– Wav2Vec 2.0 - 06L – Latent variable EBMs for structured prediction

– Wav2Vec 2.0

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:21:29 - 00:27:14
– XLSR: multilingual speech recognition - 06L – Latent variable EBMs for structured prediction

– XLSR: multilingual speech recognition

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:27:14 - 00:29:16
– Generative adversarial networks (GANs) - 06L – Latent variable EBMs for structured prediction

– Generative adversarial networks (GANs)

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:29:16 - 00:37:24
– Mode collapse - 06L – Latent variable EBMs for structured prediction

– Mode collapse

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:37:24 - 00:41:45
– Non-contrastive methods - 06L – Latent variable EBMs for structured prediction

– Non-contrastive methods

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:41:45 - 00:43:19
– BYOL: bootstrap your own latent - 06L – Latent variable EBMs for structured prediction

– BYOL: bootstrap your own latent

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:43:19 - 00:44:27
What does "averaging the weights over time" mean exactly? at - 06L – Latent variable EBMs for structured prediction

What does "averaging the weights over time" mean exactly? at

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:43:40 - 01:48:54
– SwAV - 06L – Latent variable EBMs for structured prediction

– SwAV

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:44:27 - 00:46:45
– Barlow twins - 06L – Latent variable EBMs for structured prediction

– Barlow twins

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:46:45 - 00:51:29
– SEER - 06L – Latent variable EBMs for structured prediction

– SEER

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:51:29 - 00:54:29
– Latent variable models in practice - 06L – Latent variable EBMs for structured prediction

– Latent variable models in practice

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:54:29 - 00:57:34
,,,, hi from Turkey :) - 06L – Latent variable EBMs for structured prediction

,,,, hi from Turkey :)

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:56:35 - 01:48:54
– DETR - 06L – Latent variable EBMs for structured prediction

– DETR

06L – Latent variable EBMs for structured prediction
2021年07月28日 
00:57:34 - 01:01:21
– Structured prediction - 06L – Latent variable EBMs for structured prediction

– Structured prediction

06L – Latent variable EBMs for structured prediction
2021年07月28日 
01:01:21 - 01:04:53
– Factor graph - 06L – Latent variable EBMs for structured prediction

– Factor graph

06L – Latent variable EBMs for structured prediction
2021年07月28日 
01:04:53 - 01:12:47
Just a small doubt at  (at end of factor graph), when Yann mentioned that the algo is dp and it is in linear time.But the way he explained the algo, it was more like Dijkstras greedy search, which is O(V log E). As far as I remember, Dp based shortest path that work on network exhaust ively, have O(VE) time complexity, like bellman-ford. Please do correct me if I am wrong. I know this isn't of much concern here, but it bugged me a bit, thus wanted to clarify. Thank you. - 06L – Latent variable EBMs for structured prediction

Just a small doubt at (at end of factor graph), when Yann mentioned that the algo is dp and it is in linear time.But the way he explained the algo, it was more like Dijkstras greedy search, which is O(V log E). As far as I remember, Dp based shortest path that work on network exhaust ively, have O(VE) time complexity, like bellman-ford. Please do correct me if I am wrong. I know this isn't of much concern here, but it bugged me a bit, thus wanted to clarify. Thank you.

06L – Latent variable EBMs for structured prediction
2021年07月28日 
01:11:30 - 01:48:54
– Viterbi algorithm whiteboard time - 06L – Latent variable EBMs for structured prediction

– Viterbi algorithm whiteboard time

06L – Latent variable EBMs for structured prediction
2021年07月28日 
01:12:47 - 01:30:24
– Graph transformer networks - 06L – Latent variable EBMs for structured prediction

– Graph transformer networks

06L – Latent variable EBMs for structured prediction
2021年07月28日 
01:30:24 - 01:46:48
– Graph composition, transducers - 06L – Latent variable EBMs for structured prediction

– Graph composition, transducers

06L – Latent variable EBMs for structured prediction
2021年07月28日 
01:46:48 - 01:48:38
– Final remarks - 06L – Latent variable EBMs for structured prediction

– Final remarks

06L – Latent variable EBMs for structured prediction
2021年07月28日 
01:48:38 - 01:48:54

Alfredo Canziani

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