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www.stefanom.io | ||
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blog.evjang.com
This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and ... |
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www.depthfirstlearning.com
[AI summary] The provided text is a comprehensive set of notes and exercises covering various topics in Generative Adversarial Networks (GANs) and their improvements, including standard GANs, Wasserstein GANs (WGANs), and WGAN with Gradient Penalty (WGAN-GP). The content includes theoretical explanations, practical implementation tasks, and discussion of challenges and solutions in training GANs. Key topics include the mathematical foundations of GANs, the limitations of standard GANs (such as mode collapse and sensitivity to hyperparameters), the introduction of WGANs to address these issues through the Wasserstein distance, and further improvements with WGAN-GP to mitigate problems like weight clipping instability. The text also includes exercises for calc... |
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sebastianraschka.com
I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs). |
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fodsi.us
[AI summary] The ML4A Virtual Workshop explores how machine learning enhances classical algorithms through data-driven approaches, featuring talks on deep generative models, model-based deep learning, and learning-augmented algorithms. |
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