Deep Generative Models: Variational Autoencoders & GANs
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Covers Variational Autoencoders, a probabilistic approach to autoencoders for data generation and feature representation, with applications in Natural Language Processing.
Delves into uniformly accurate hydrodynamic models for kinetic equations using machine learning, covering Boltzmann equation, moment methods, and numerical results.
Covers the basics of text generation and the challenges of evaluating generated text using content overlap metrics, model-based metrics, and human evaluations.
Explores natural language generation, focusing on building systems that produce coherent text for human consumption using various decoding methods and evaluation metrics.
