Completed
– Joint embedding architectures
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Latent Variable EBMs for Structured Prediction
Automatically move to the next video in the Classroom when playback concludes
- 1 – Welcome to class
- 2 – Training of an EBM
- 3 – Contrastive vs. regularised / architectural methods
- 4 – General margin loss
- 5 – List of loss functions
- 6 – Generalised additive margin loss
- 7 – Joint embedding architectures
- 8 – Wav2Vec 2.0
- 9 – XLSR: multilingual speech recognition
- 10 – Generative adversarial networks GANs
- 11 – Mode collapse
- 12 – Non-contrastive methods
- 13 – BYOL: bootstrap your own latent
- 14 – SwAV
- 15 – Barlow twins
- 16 – SEER
- 17 – Latent variable models in practice
- 18 – DETR
- 19 – Structured prediction
- 20 – Factor graph
- 21 – Viterbi algorithm whiteboard time
- 22 – Graph transformer networks
- 23 – Graph composition, transducers
- 24 – Final remarks
