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peterbloem.nl | ||
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resources.paperdigest.org
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| | | | | The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. Paper Digest Team analyze all papers published on NIPS in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically construc | |
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iclr-blogposts.github.io
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| | | | | This blog post explores the interplay between the Data Processing Inequality (DPI), a cornerstone concept in information theory, and Function-Space Variational Inference (FSVI) within the context of Bayesian deep learning. The DPI governs the transformation and flow of information through stochastic processes, and its unique connection to FSVI is employed to highlight FSVI's focus on Bayesian predictive posteriors over parameter space. The post examines various forms of the DPI, including the KL divergence based DPI, and provides intuitive examples and detailed proofs. It also explores the equality case of the DPI to gain a deeper understanding. The connection between DPI and FSVI is then established, showing how FSVI can measure a predictive divergence independent of parameter symmetries. The post relates FSVI to knowledge distillation and label entropy regularization, highlighting the practical relevance of the theoretical concepts. Throughout the post, theoretical concepts are intertwined with intuitive explanations and mathematical rigor, offering a comprehensive understanding of these complex topics. By examining these concepts in depth, the post provides valuable insights for both theory and practice in machine learning. | |
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fa.bianp.net
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| | | | | The Langevin algorithm is a simple and powerful method to sample from a probability distribution. It's a key ingredient of some machine learning methods such as diffusion models and differentially private learning. In this post, I'll derive a simple convergence analysis of this method in the special case when the ... | |
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tcode2k16.github.io
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| | | a random blog about cybersecurity and programming | ||
