Completed
– Graph composition, transducers
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Classroom Contents
Latent Variable EBMs for Structured Prediction
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- 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
