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Titlebook: Hardware-Aware Probabilistic Machine Learning Models; Learning, Inference Laura Isabel Galindez Olascoaga,Wannes Meert,Maria Book 2021 The

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發(fā)表于 2025-3-25 07:20:43 | 只看該作者
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發(fā)表于 2025-3-25 07:49:14 | 只看該作者
Background,d devices considered in this book. More specifically, it discusses how some of the axioms of probability theory led to the proposal of Bayesian Networks, and it explains how to use these models for inference. It also provides an overview of the main properties of Probabilistic Circuits and explains
23#
發(fā)表于 2025-3-25 13:54:41 | 只看該作者
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發(fā)表于 2025-3-25 19:35:15 | 只看該作者
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發(fā)表于 2025-3-25 20:30:21 | 只看該作者
Hardware-Aware Probabilistic Circuits,fficient inference and that can explicitly trade-off expressiveness and complexity at learning time. This hardware-aware approach is equipped to induce the cost versus accuracy trade-off brought about by the hardware scaling at different levels of abstraction of the system. The hardware-aware Probab
26#
發(fā)表于 2025-3-26 03:47:41 | 只看該作者
Run-Time Strategies,issing features and the need for low-cost operation. The first part of this chapter demonstrates how the Pareto-optimal cost versus accuracy trade-off derived with the techniques in Chap. . can be used in a run-time scenario and remain robust to missing features due to failing sensors; and the secon
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發(fā)表于 2025-3-26 04:55:23 | 只看該作者
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發(fā)表于 2025-3-26 08:32:13 | 只看該作者
Laura Isabel Galindez Olascoaga,Wannes Meert,Marian Verhelstction in a ‘theory of mind mechanism’, resulting in ‘mindblindness’. To establish this point, the chapter takes up two interesting ideas in the ‘Theory of Mind’ literature, but purged of their Cartesianism: first that the study of autism does indeed provide us with critical insights into the develop
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發(fā)表于 2025-3-26 13:04:52 | 只看該作者
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