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johncarlosbaez.wordpress.com | ||
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francisbach.com
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| | | | | [AI summary] This text discusses the scaling laws of optimization in machine learning, focusing on asymptotic expansions for both strongly convex and non-strongly convex cases. It covers the derivation of performance bounds using techniques like Laplace's method and the behavior of random minimizers. The text also explains the 'weird' behavior observed in certain plots, where non-strongly convex bounds become tight under specific conditions. The analysis connects theoretical results to practical considerations in optimization algorithms. | |
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www.jeremykun.com
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| | | | | Decidability Versus Efficiency In the early days of computing theory, the important questions were primarily about decidability. What sorts of problems are beyond the power of a Turing machine to solve? As we saw in our last primer on Turing machines, the halting problem is such an example: it can never be solved a finite amount of time by a Turing machine. However, more recently (in the past half-century) the focus of computing theory has shifted away from possibility in favor of determining feasibility. | |
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pressron.wordpress.com
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| | | | | Abstract: Machine and language models of computation differ so greatly in the computational complexity properties of their representation that they form two distinct classes that cannot be directly compared in a meaningful way. While machine models are self-contained, the properties of the language models indicate that they require a computationally powerful collaborator, and are better... | |
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jdh.hamkins.org
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| | | This will be a series of lectures on the philosophy of mathematics, given at Oxford University, Michaelmas term 2018. The lectures are mainly intended for undergraduate students preparing for exam ... | ||
