Lecture: Group Sparsity, World Model, and Generative Adversarial Networks (GANs)

Lecture: Group Sparsity, World Model, and Generative Adversarial Networks (GANs)

Group sparsity, world model, and generative adversarial networks (GANs), we discussed discriminative recurrent sparse auto-encoders and group sparsity. The main idea was how to combine sparse coding with discriminative training. We went through how to structure a network with a recurrent autoencoder similar to LISTA and a decoder. Then we discussed how to use group sparsity to extract invariant features.

Week 9 – Lecture: Group sparsity, world model, and generative adversarial networks (GANs)

0:00:00 – Week 9 – Lecture

LECTURE Part A: http://bit.ly/pDL-en-09-1 We discussed discriminative recurrent sparse auto-encoders and group sparsity. The main idea was how to combine sparse coding with discriminative training. We went through how to structure a network with a recurrent autoencoder similar to LISTA and a decoder. Then we discussed how to use group sparsity to extract invariant features. 0:00:35 – Discriminative Recurrent Sparse Auto-Encoder and Group Sparsity 0:15:18 – AE With Group Sparsity: Questions and Clarification 0:30:34 – Convolutional RELU with Group Sparsity

LECTURE Part B: http://bit.ly/pDL-en-09-2 In this section, we talked about the World Models for autonomous control including the neural network architecture and training schema. Then, we discussed the difference between World Models and Reinforcement Learning (RL). Finally, we studied Generative Adversarial Networks (GANs) in terms of energy-based model with the contrastive method. 0:42:06 – Learning World Models for Autonomous Control 1:06:33 – Reinforcement Learning 1:30:30 – Generative Adversarial Network

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