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Machine Learning and Knowledge Discovery in Databases

Overview of attention for book
Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks
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    Chapter 2 Factorized Structured Regression for Large-Scale Varying Coefficient Models
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    Chapter 3 Ordinal Quantification Through Regularization
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    Chapter 4 Random Similarity Forests
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    Chapter 5 Spectral Ranking with Covariates
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    Chapter 6 Truly Unordered Probabilistic Rule Sets for Multi-class Classification
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    Chapter 7 From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm
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    Chapter 8 Sparse Horseshoe Estimation via Expectation-Maximisation
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    Chapter 9 Structure-Preserving Gaussian Process Dynamics
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    Chapter 10 Summarizing Data Structures with Gaussian Process and Robust Neighborhood Preservation
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    Chapter 11 Optimization of Annealed Importance Sampling Hyperparameters
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    Chapter 12 Bayesian Nonparametrics for Sparse Dynamic Networks
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    Chapter 13 A Pre-screening Approach for Faster Bayesian Network Structure Learning
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    Chapter 14 On Projectivity in Markov Logic Networks
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    Chapter 15 A Non-parametric Bayesian Approach for Uplift Discretization and Feature Selection
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    Chapter 16 Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages
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    Chapter 17 Learning Optimal Transport Between Two Empirical Distributions with Normalizing Flows
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    Chapter 18 Feature-Robust Optimal Transport for High-Dimensional Data
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    Chapter 19 Penalized FTRL with Time-Varying Constraints
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    Chapter 20 Rethinking Exponential Averaging of the Fisher
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    Chapter 21 Mixed Integer Linear Programming for Optimizing a Hopfield Network
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    Chapter 22 Learning to Control Local Search for Combinatorial Optimization
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    Chapter 23 Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-Based Policy Learning
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    Chapter 24 Learning Optimal Decision Trees Under Memory Constraints
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    Chapter 25 SaDe: Learning Models that Provably Satisfy Domain Constraints
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    Chapter 26 On the Generalization of Neural Combinatorial Optimization Heuristics
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    Chapter 27 Time Constrained DL8.5 Using Limited Discrepancy Search
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    Chapter 28 Block-Level Surrogate Models for Inference Time Estimation in Hardware-Aware Neural Architecture Search
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    Chapter 29 FASE : A Fast, Accurate and Seamless Emulator for Custom Numerical Formats
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    Chapter 30 GNNSampler: Bridging the Gap Between Sampling Algorithms of GNN and Hardware
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    Chapter 31 Training Parameterized Quantum Circuits with Triplet Loss
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    Chapter 32 Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random Forests
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    Chapter 33 CGPM: Poverty Mapping Framework Based on Multi-Modal Geographic Knowledge Integration and Macroscopic Social Network Mining
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    Chapter 34 Bayesian Multi-head Convolutional Neural Networks with Bahdanau Attention for Forecasting Daily Precipitation in Climate Change Monitoring
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    Chapter 35 Cubism : Co-balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer
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    Chapter 36 Go Green: A Decision-Tree Framework to Select Optimal Box-Sizes for Product Shipments
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    Chapter 37 An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning
Attention for Chapter 37: An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

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2 news outlets
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6 X users

Readers on

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10 Mendeley
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Chapter title
An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning
Chapter number 37
Book title
Machine Learning and Knowledge Discovery in Databases
Published in
arXiv, May 2023
DOI 10.1007/978-3-031-26419-1_37
Book ISBNs
978-3-03-126418-4, 978-3-03-126419-1
Authors

Puech, Alban, Read, Jesse, Alban Puech, Jesse Read

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 20%
Professor 1 10%
Unknown 7 70%
Readers by discipline Count As %
Engineering 2 20%
Mathematics 1 10%
Unknown 7 70%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 May 2023.
All research outputs
#1,860,340
of 24,857,051 outputs
Outputs from arXiv
#30,114
of 1,010,863 outputs
Outputs of similar age
#36,257
of 391,269 outputs
Outputs of similar age from arXiv
#1,628
of 38,694 outputs
Altmetric has tracked 24,857,051 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,010,863 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 391,269 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 38,694 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.

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