仪式
发表于 2025-3-28 14:50:34
,Recipe for Fast Large-Scale SVM Training: Polishing, Parallelism, and More RAM!,both approaches to design an extremely fast dual SVM solver. We fully exploit the capabilities of modern compute servers: many-core architectures, multiple high-end GPUs, and large random access memory. On such a machine, we train a large-margin classifier on the ImageNet data set in 24 min.
AXIS
发表于 2025-3-28 22:49:51
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友好关系
发表于 2025-3-28 23:39:14
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反馈
发表于 2025-3-29 04:43:28
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Temporal-Lobe
发表于 2025-3-29 07:38:57
https://doi.org/10.1007/978-981-97-2393-5both approaches to design an extremely fast dual SVM solver. We fully exploit the capabilities of modern compute servers: many-core architectures, multiple high-end GPUs, and large random access memory. On such a machine, we train a large-margin classifier on the ImageNet data set in 24 min.
傀儡
发表于 2025-3-29 14:02:35
https://doi.org/10.1007/978-3-658-46377-9literature, from straightforward state aggregation to deep learned representations, and sketch challenges that arise when combining model-based reinforcement learning with abstraction. We further show how various methods deal with these challenges and point to open questions and opportunities for further research.
Arrhythmia
发表于 2025-3-29 15:37:59
1865-0929 Mechelen, Belgium, in November 2022..The 11 papers presented in this volume were carefully reviewed and selected from 134 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning
Dungeon
发表于 2025-3-29 23:19:17
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OVERT
发表于 2025-3-30 02:19:06
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抱负
发表于 2025-3-30 06:52:30
,A View on Model Misspecification in Uncertainty Quantification,ys exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecification is reliable or not. In this paper, we argue that model misspecification should receive more attention, by providing thought experiments and contextualizing these with relevant literature.