Intro
Preface
Organization
Contents
Part V
Automating Machine Learning, Optimization, and Feature Engineering
PuzzleShuffle: Undesirable Feature Learning for Semantic Shift Detection
1 Introduction
2 Related Work
2.1 Out-of-Distribution Detection
2.2 Data Augmentation
2.3 Uncertainty Calibration
3 Preliminaries
3.1 The Effects by Perturbation
3.2 Adversarial Undesirable Feature Learning
4 Proposed Method
4.1 PuzzleShuffle Augmentation
4.2 Adaptive Label Smoothing
4.3 Motivation
5 Experiments
5.1 Experimental Settings

5.2 Compared Methods
5.3 Results
5.4 Analysis
6 Conclusion
References
Enabling Machine Learning on the Edge Using SRAM Conserving Efficient Neural Networks Execution Approach
1 Introduction
2 Background and Related Work
2.1 Deep Model Compression
2.2 Executing Neural Networks on Microcontrollers
3 Efficient Neural Network Execution Approach Design
3.1 Tensor Memory Mapping (TMM) Method Design
3.2 Loading Fewer Tensors and Tensors Re-usage
3.3 Finding the Cheapest NN Graph Execution Sequence
3.4 Core Algorithm
4 Experimental Evaluation
4.1 SRAM Usage

4.2 Model Performance
4.3 Inference Time and Energy Consumption
5 Conclusion
References
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge
1 Introduction
2 Challenge Setting
2.1 Phases
2.2 Protocol
2.3 Datasets
2.4 Metrics
2.5 Platform, Hardware and Limitations
2.6 Baseline
2.7 Results
3 Post Challenge Experiments
3.1 Reproducibility
3.2 Overfitting and Generalisation
3.3 Comparison to Open Source AutoML Solutions
3.4 Impact of Time Budget
3.5 Dataset Difficulty
4 Conclusion and Future Work
References

Methods for Automatic Machine-Learning Workflow Analysis
1 Introduction
2 Problem Definition
3 Related Work
4 Residual Graph-Level Graph Convolutional Networks
5 Datasets
6 Workflow Similarity
7 Structural Performance Prediction
8 Component Refinement and Suggestion
9 Conclusion
References
ConCAD: Contrastive Learning-Based Cross Attention for Sleep Apnea Detection
1 Introduction
2 Related Work
2.1 Sleep Apnea Detection
2.2 Attention-Based Feature Fusion
2.3 Contrastive Learning
3 Methodology
3.1 Expert Feature Extraction and Data Augmentation

3.2 Feature Extractor
3.3 Cross Attention
3.4 Contrastive Learning.
4 Experiments and Results
4.1 Datasets
4.2 Compared Methods
4.3 Experiment Setup
4.4 Results and Discussions
5 Conclusions and Future Work
References
Machine Learning Based Simulations and Knowledge Discovery
DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Network
1 Introduction
2 Related Work
3 Methods
3.1 Problem Definition
3.2 Deep Parameterization Emulator
3.3 Transfer Scheme
3.4 Training
4 Experiments
4.1 Datasets