Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP22-YouTube Speaker: Jiachen Zhu (朱家晨) #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks #SSL #self-supervised learning #joint-embedding methods #JEM

One more video for the DLSP21 saga

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Welcome to class #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Welcome to class #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Welcome to class 00:06:55 – Training methods revisited 00:08:03 – Architectural methods 00:12:00 – 1. PCA 00:18:04 – Q&A on Definitions: Labels, (un)conditional, and (un, self)supervised learning 00:25:31 – 2. Auto-encoder with Bottleneck 00:27:40 – 3. K-Means 00:34:40 – 4. Gaussian mixture model 00:41:37 – Regularized EBM 00:52:08 – Yann out of context 00:53:24 – Q&A on Norms and Posterior: when the student is thinking too far ahead 00:53:58 – 1. Unconditional regularized latent variable EBM: Sparse coding 01:06:10 – Sparse modeling on MNIST & natural patches 01:12:18 – 2. Amortized inference 01:17:02 – ISTA algorithm & RNN Encoder 01:26:56 – 3. Convolutional sparce coding 01:36:37 – 4. Video prediction: very briefly 01:39:22 – 5. VAE: an intuitive interpretation 01:48:34 – Helpful whiteboard stuff 01:52:35 – Another interpretation #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Welcome to class 00:00:45 – GANs revisited 00:17:07 – Self-supervised learning: a broader purpose 00:31:59 – Sparse modeling 00:43:25 – Amortized inference 00:51:21 – Convolutional sparse modeling (with group sparsity 00:55:12 – Discriminant recurrent sparse AE 00:57:26 – Other self-supervised learning techniques 00:58:45 – Group sparsity 01:07:47 – Regularization through temporal consistency 01:12:09 – VAE: intuitive interpretation 01:26:13 – VAE: probabilistic variational approximation-based interpretation #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Motivation for reasoning & planning 00:09:11 – Inference through energy minimization 00:18:08 – Disclaimer 00:19:02 – Planning through energy minimization 00:32:59 – Q&A Optimal control diagram 00:39:23 – Differentiable associative memory and attention 01:01:03 – Transformers 01:08:14 – Q&A Other differentiable attention architectures 01:10:32 – Transformer architecture 01:27:54 – Transformer applications: 1. Multilingual transformer Architecture XML-R 01:30:16 – 2. Supervised symbol manipulation 01:32:14 – 3. NL understanding & generation 01:36:51 – 4. DETR 01:46:47 – Planing through optimal control 01:55:37 – Conclusion #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Alfredo Canziani Chapters 00:00 – Welcome to class #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks #Kelley-Bryson

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

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Welcome to class 00:00:39 – Predictive models 00:02:25 – Multi-output system 00:06:36 – Notation (factor graph) 00:07:41 – The energy function F(x, y) 00:08:53 – Inference 00:11:59 – Implicit function 00:15:53 – Conditional EBM 00:16:24 – Unconditional EBM 00:19:18 – EBM vs. probabilistic models 00:21:33 – Do we need a y at inference? 00:23:29 – When inference is hard 00:25:02 – Joint embeddings 00:28:29 – Latent variables 00:33:54 – Inference with latent variables 00:37:58 – Energies E and F 00:42:35 – Preview on the EBM practicum 00:44:30 – From energy to probabilities 00:50:37 – Examples: K-means and sparse coding 00:53:56 – Limiting the information capacity of the latent variable 00:57:24 – Training EBMs 01:04:02 – Maximum likelihood 01:13:58 – How to pick β? 01:17:28 – Problems with maximum likelihood 01:20:20 – Other types of loss functions 01:26:32 – Generalised margin loss 01:27:22 – General group loss 01:28:26 – Contrastive joint embeddings 01:34:51 – Denoising or mask autoencoder 01:46:14 – Summary and final remarks #PyTorch #NYU #Yann LeCun #Deep Learning #neural networks