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liorsinai.github.io | ||
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iclr-blogposts.github.io
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| | | | | The product between the Hessian of a function and a vector, the Hessian-vector product (HVP), is a fundamental quantity to study the variation of a function. It is ubiquitous in traditional optimization and machine learning. However, the computation of HVPs is often considered prohibitive in the context of deep learning, driving practitioners to use proxy quantities to evaluate the loss geometry. Standard automatic differentiation theory predicts that the computational complexity of an HVP is of the same order of magnitude as the complexity of computing a gradient. The goal of this blog post is to provide a practical counterpart to this theoretical result, showing that modern automatic differentiation frameworks, JAX and PyTorch, allow for efficient computat... | |
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matbesancon.xyz
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| | | | | What can automated gradient computations bring to mathematical optimizers, what does it take to compute? | |
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jingnanshi.com
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| | | | | Tutorial on automatic differentiation | |
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www.onlandscape.co.uk
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| | | [AI summary] The article discusses the privacy and cookie policies of the online magazine 'On Landscape', which focuses on landscape photography, and includes information about its registration and social media presence. | ||
