Generative Modeling: Then, Now and Tomorrow?

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Related lectures (30)

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Deep Generative Models: Variational Autoencoders & GANs

Explores Variational Autoencoders and Generative Adversarial Networks for deep generative modeling.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Generative Adversarial Networks: Data Synthesis Techniques

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.

Generative Models: Trajectory Forecasting

Explores generative models for trajectory forecasting in autonomous vehicles, including discriminative vs generative models, VAES, GANS, and case studies.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Polynomial Networks: Spectral Bias Analysis

Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Convolutional Neural Networks

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.