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Machine Learning and Knowledge Discovery in Databases

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Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

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    Book Overview
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    Chapter 1 Exploiting the Earth’s Spherical Geometry to Geolocate Images
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    Chapter 2 Continual Rare-Class Recognition with Emerging Novel Subclasses
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    Chapter 3 Unjustified Classification Regions and Counterfactual Explanations in Machine Learning
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    Chapter 4 Shift Happens: Adjusting Classifiers
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    Chapter 5 Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data
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    Chapter 6 Cost Sensitive Evaluation of Instance Hardness in Machine Learning
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    Chapter 7 Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification
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    Chapter 8 PP-PLL: Probability Propagation for Partial Label Learning
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    Chapter 9 Neural Message Passing for Multi-label Classification
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    Chapter 10 Assessing the Multi-labelness of Multi-label Data
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    Chapter 11 Synthetic Oversampling of Multi-label Data Based on Local Label Distribution
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    Chapter 12 Distributed Learning of Non-convex Linear Models with One Round of Communication
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    Chapter 13 SLSGD: Secure and Efficient Distributed On-device Machine Learning
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    Chapter 14 Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning
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    Chapter 15 Importance Weighted Generative Networks
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    Chapter 16 Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks
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    Chapter 17 LYRICS: A General Interface Layer to Integrate Logic Inference and Deep Learning
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    Chapter 18 Deep Eyedentification: Biometric Identification Using Micro-movements of the Eye
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    Chapter 19 Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
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    Chapter 20 Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics
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    Chapter 21 Multitask Hopfield Networks
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    Chapter 22 Meta-Learning for Black-Box Optimization
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    Chapter 23 Training Discrete-Valued Neural Networks with Sign Activations Using Weight Distributions
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    Chapter 24 Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural Networks
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    Chapter 25 Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees
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    Chapter 26 $$L_0$$ -ARM: Network Sparsification via Stochastic Binary Optimization
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    Chapter 27 Learning with Random Learning Rates
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    Chapter 28 FastPoint: Scalable Deep Point Processes
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    Chapter 29 Single-Path NAS: Designing Hardware-Efficient ConvNets in Less Than 4 Hours
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    Chapter 30 Scalable Large Margin Gaussian Process Classification
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    Chapter 31 Integrating Learning and Reasoning with Deep Logic Models
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    Chapter 32 Neural Control Variates for Monte Carlo Variance Reduction
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    Chapter 33 Data Association with Gaussian Processes
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    Chapter 34 Incorporating Dependencies in Spectral Kernels for Gaussian Processes
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    Chapter 35 Deep Convolutional Gaussian Processes
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    Chapter 36 Bayesian Generalized Horseshoe Estimation of Generalized Linear Models
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    Chapter 37 Fine-Grained Explanations Using Markov Logic
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    Chapter 38 Unsupervised Sentence Embedding Using Document Structure-Based Context
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    Chapter 39 Copy Mechanism and Tailored Training for Character-Based Data-to-Text Generation
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    Chapter 40 NSEEN: Neural Semantic Embedding for Entity Normalization
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    Chapter 41 Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts
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    Chapter 42 A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset
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    Chapter 43 Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model
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Title
Machine Learning and Knowledge Discovery in Databases
Published by
Springer International Publishing, July 2020
DOI 10.1007/978-3-030-46147-8
ISBNs
978-3-03-046146-1, 978-3-03-046147-8
Editors

Brefeld, Ulf, Fromont, Elisa, Hotho, Andreas, Knobbe, Arno, Maathuis, Marloes, Robardet, Céline

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