Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, Virtual Event, September ... in Computer and Information Science, 1524) 3030937356, 9783030937355

Table of contents :
Preface
Organization
Contents – Part I
Contents – Part II
Advances in Interpretable Machine Learning and Artificial Intelligence
Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021)
Acknowledgements
chPart1
Organization
AIMLAI 2021 Organizers
Program Committee
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
1 Introduction
2 Related Work
3 Post-hoc Local Explanations with LIME
4 Finding Suitable Segmentation Mappings
4.1 Using Static Windows
4.2 Using the Matrix Profile
4.3 Using the SAX Transformation
4.4 Comparing the Segmentation Algorithms
5 Evaluating TS-MULE on Time Series Forecasting
6 Conclusion
References
Interpretable Models via Pairwise Permutations Algorithm
1 Introduction
2 Related Work
3 Pairwise Permutations Algorithm
3.1 Notation
3.2 Intuition
3.3 Definition
3.4 Expected Difference
4 Simulations with Toy Dataset
4.1 Results
5 Microbial Biomarkers for Type 2 Diabetes Mellitus
5.1 Results
6 Conclusions
References
A Classification of Anomaly Explanation Methods
1 Introduction
2 Existing Comparison Criteria of Anomaly Explanations
3 Taxonomy of Anomaly Explanation Methods
3.1 Anomaly Explanation by Feature Importance
3.2 Anomaly Explanation by Feature Values
3.3 Anomaly Explanation by Data Points Comparisons
3.4 Anomaly Explanation by Structure Analysis
4 Conclusion
References
Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots
1 Introduction
2 Related Work
3 Methodology
3.1 ICE Plot Replication
3.2 ICE Feature Impact
3.3 In-Distribution ICE Feature Impact
4 Real Data
4.1 Complementary to Feature Importance
4.2 Interpretability: Analogous to Linear Regression Coefficients
4.3 Quantifying Heterogeneity and Non-linearity
5 Discussion
A ICE Plots for Cervical Cancer Data
B c-ICE Plots for Cervical Cancer Data
C Feature Impact Table for Cervical Cancer Data
D Heterogeneity and Non-linearity of ICE Feature Impact for Cervical Cancer Data
References
Reject and Cascade Classifier with Subgroup Discovery for Interpretable Metagenomic Signatures
1 Introduction
2 Related Work
3 Applications of Subgroup Discovery to Metagenomics for Phenotype Status Prediction
3.1 Subgroup Discovery
3.2 Overview and Concepts of the Q-Classifier
3.3 Statistical Metrics and Optimal Union
3.4 Rejection and Delegation Concepts to Adapt SD for Prediction
4 Experiments
4.1 Survey of Existing Metagenomics Datasets
4.2 Benchmark on Real-World Metagenomic Data
5 Conclusion
A Example of Metagenomic Abundance Table
B Compositional Data and Log-Ratio Transformations
C Q-Classifier Schemes and Overview
D Q-Classifier Data Processing
E Pseudo Code of Optimal Union
F Simulated Dataset Used to Train Taxa Classifier
G Pseudo Code of Q-Classifier's Training and Classification Stage
H Rules Coverage Analysis on the Cirrhosis Dataset
References
Demystifying Graph Neural Network Explanations
1 Introduction
2 Terminology and Concepts
2.1 Terminology
2.2 Synthetic Data
3 Pitfalls of Evaluation and Possible Remedies
3.1 Pitfall 1: Data Generation
3.2 Pitfall 2: Evaluation Metrics
3.3 Pitfall 3: Threshold Application
4 Conclusion
A Background on GNNs and Perturbation-Based Explainer Methods
References
On the Transferability of Neural Models of Morphological Analogies
1 Introduction
2 Proposed Approach
2.1 Classification Model
2.2 Embedding Model
3 Experiments
3.1 Datasets and Augmentation
3.2 Transferability Results
3.3 Discussion
4 Toward a Multilingual Model
4.1 Models
4.2 Results
4.3 Discussion
5 Conclusion
References
Behavior of k-NN as an Instance-Based Explanation Method
1 Introduction
2 Related Work
3 Experiments and Results
3.1 The Choice of Representation
3.2 Effect of Removing Explanatory Instances
4 Conclusion
References
Enhancing Performance of Occlusion-Based Explanation Methods by a Hierarchical Search Method on Input Images
1 Introduction
2 Proposed Method
3 Experimental Results
4 Conclusion
References
Post-hoc Counterfactual Generation with Supervised Autoencoder
1 Introduction
2 Post-hoc Counterfactual Generation
3 Counterfactuals Based on Latent Prototypes
4 Experiments and Results
4.1 Metrics
4.2 Results
5 Conclusion and Future Work
6 Supplementary Material
References
Parallel, Distributed, and Federated Learning
Workshop on Parallel, Distributed, and Federated Learning (PDFL 2021)
chPart2
Organization
PDFL 2021 Chairs
Program Committee
Differentially Private Learning from Label Proportions
1 Introduction
2 Related Work
3 Algorithm
4 Experimental Evaluation
5 Conclusion
References
Approaches to Uncertainty Quantification in Federated Deep Learning
1 Introduction
2 Preliminaries
2.1 Uncertainty Quantification in Deep Learning
2.2 Federated Deep Learning
3 Leveraging Uncertainty in Federated Deep Learning
3.1 Ensembles in Federated Deep Learning
3.2 MC-Dropout in Federated Deep Learning
3.3 SWAG in Federated Deep Learning
4 Empirical Evaluation
5 Discussion and Conclusion
A Appendix
References
Optimized Federated Learning on Class-Biased Distributed Data Sources
1 Introduction
2 Preliminaries
2.1 Federated Learning
2.2 Statistical Heterogeneity
3 Related Work
4 Methods
4.1 Federated Learning Algorithms
4.2 Proposed Approach
5 Experiments
5.1 Dataset and Data Distribution
5.2 Experimental Setup
6 Results
7 Conclusion
References
Splitting Algorithms for Federated Learning
1 Introduction
2 Background
3 FL as Operator Splitting
3.1 FedAvg as Forward-Backward Splitting
3.2 FedProx as Backward-Backward Splitting
3.3 FedSplit as Peaceman-Rachford Splitting
3.4 FedPi as Douglas-Rachford Splitting
4 Unification
5 Conclusions
A Experimental Setup
A.1 Experimental setup: Least Squares and Logistic Regression
A.2 Experimental setup: MNIST datasets
B Proofs
C Over-relaxation
References
Migrating Models: A Decentralized View on Federated Learning
1 Introduction
1.1 Related Work
1.2 Our Contribution
2 Migrating Models (MM)
3 Experiments
3.1 Results
4 Discussion
4.1 Convergence
4.2 Privacy
5 Conclusion
References
Graph Embedding and Mining
Workshop on Graph Embedding and Mining (GEM 2021)
chPart3
Organization
GEM 2021 Organizers
GEM 2021 Program Committee
The Effects of Randomness on the Stability of Node Embeddings
1 Introduction
2 Related Work
3 Experimental Framework
4 Results
5 Discussion
6 Conclusion
A Experimental Setup
A.1 Datasets
A.2 Implementations and Parameter Settings
B Experiments on Geometric Stability
B.1 Measures for Geometric Stability
B.2 Experimental Results
C Additional Results on Downstream Stability
C.1 Node Classification
C.2 Link Prediction
References
Graph Homomorphism Features: Why Not Sample?
1 Introduction
2 Graph Homomorphism Numbers
3 Graph Homomorphism Densities
4 Graph Classification Using Homomorphism Features
5 Conclusion
References
Neural Maximum Independent Set
1 Introduction
2 Related Work
3 Preliminaries
4 Method
4.1 Using Expert Iteration to Solve Max Independent Set
4.2 Evaluation of the Neural Network
5 Experimental Results
5.1 Boosting the Learning Phase with Good Features
6 Conclusion
A Results on other instances
B Stochastic exploration method allows to find various good solutions
C GNNs can export expertise on Max Independent Set from a small graph to a larger graph
References
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
1 Introduction
2 Related Works
3 Methods
3.1 Preliminary Graph Feature Extraction
3.2 Fea2Fea-Single: Single Feature Predicts Single Feature
3.3 Fea2Fea-Multiple: Mutiple Features Predict Single Feature
3.4 Model Architecture
4 Experiments
4.1 Results
5 Conclusions and Future Works
A Algorithm Pseudocode
B Hyper-parameter Tunning
C Visualization
D Supplementary Results on Feature Prediction
E Graph Embedding Analysis
F Generalization
References
Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM Tree
1 Introduction
2 Method
2.1 Framework
2.2 Baselines
2.3 Models
3 Experiments and Results
4 Conclusion
References
Towards Mining Generalized Patterns from RDF Data and a Domain Ontology
1 Introduction
2 Related Work
3 Ontology-Based Graph Pattern Mining
3.1 Problem Statement
3.2 An OGP from the Dairy Dataset
3.3 Two Workaround Approaches
4 Evaluation of the DO-Powered Pattern Miners
5 Conclusion
References
Machine Learning for Irregular Time Series
Workshop on Machine Learning for Irregular Time Series (ML4ITS 2021)
chPart4
Organization
Workshop Co-chairs
Program Committee
Homological Time Series Analysis of Sensor Signals from Power Plants
1 Introduction
2 Primer: Persistent Homology
2.1 Homology Groups
2.2 Persistent Homology
3 Time Series Embedding
3.1 Polynomial Approximation
3.2 Smooth Manifold Construction
4 Sliding-Window Embedding
4.1 Takens' Embedding
4.2 Periodic Signals
4.3 Quasi-periodic Signals
5 Heuristic Choice of Parameters
5.1 Time Delay
5.2 Embedding Dimension M
6 Results
6.1 Persistence Representations
6.2 Classification
7 Summary
References
Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series
1 Introduction
2 Method
2.1 Continuous-Discrete Kalman Filter
2.2 Continuous-Discrete Recurrent Kalman Networks
3 Empirical Study
4 Conclusion
References
Adversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data
1 Introduction
2 Background and Notations
2.1 Sequence Generation with RNNs
2.2 Adversarial Generation
3 Adversarial Generation Algorithms: Variants
3.1 C-RNN-GAN
3.2 RCGAN
3.3 TimeGAN
3.4 DoppelGANger
4 Analysis of GAN Variants
5 Evaluation Metrics
6 Datasets
7 Experiments and Results
7.1 Performance Evaluation
8 Discussion
References
IoT, Edge, and Mobile for Embedded Machine Learning
Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021)
chPart5
Organization
ITEM 2021 Chairs
Program Committee
Towards Precomputed 1D-Convolutional Layers for Embedded FPGAs
1 Introduction
2 Related Work
3 Our Approach: Precomputed 1D-CNNs
3.1 Quantizing Activations
3.2 Splitting Convolutions
4 Evaluation
5 Conclusion and Future Work
References
Embedded Face Recognition for Personalized Services in the Assistive Robotics
1 Introduction
2 Related Work
2.1 Personalization in Assistive Robotics
2.2 Face Recognition
2.3 Low-Complexity Neural Networks
2.4 FPGA Implementations of Face Recognition
2.5 FINN
3 Context and Methodology
3.1 Model Topology
3.2 Hardware Architecture
4 Evaluation and Discussion
4.1 Experimental Setup and Training
4.2 Design Space Exploration
5 Conclusion and Future Work
References
FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics
1 Introduction
2 Related Work
3 FLight Framework
3.1 Overview
3.2 Repository of Customizable HLS Functions
3.3 TFPGA Engine
3.4 Architecture of Hardware Accelerator
4 Industrial Analytics Using FLight: A Case Study
5 Experimental Results
6 Conclusion and Future Work
References
Exploring Cell-Based Neural Architectures for Embedded Systems
1 Introduction
2 Related Work
3 Cell-Based Architecture Analysis
4 Architecture Search
4.1 Search Space
4.2 Grid Search
4.3 Architecture Evaluation
5 Exploration Results
6 Conclusion
References
Design Space Exploration of Time, Energy, and Error Rate Trade-offs for CNNs Using Accuracy-Programmable Instruction Set Processors
1 Introduction
2 Related Work
3 Fundamentals
3.1 Anytime Instruction Processors
3.2 Deep Learning and CNNs
4 Design Space Exploration
4.1 Error Rate R
4.2 Execution Time T
4.3 Energy E
5 Experiments and Results
5.1 Comparison of Anytime Instructions Against IEEE 754 Single Precision and Variable Precision Formats
5.2 DSE Setup
5.3 Search Space Analysis
5.4 Experimental Results
6 Conclusion
References
Ultra-low Power Machinery Fault Detection Using Deep Neural Networks
1 Introduction
2 Approach and Limitations
3 State of the Art
4 Neural Network Models
4.1 Artificial Neural Networks
4.2 Spiking Neural Networks
5 Evaluation
6 Conclusion and Future Work
References
SPNC: Fast Sum-Product Network Inference
1 Introduction
2 Background
2.1 Sum-Product Networks
2.2 MLIR
3 Approach
3.1 MLIR Dialect Design
3.2 CPU Compilation Flow
3.3 GPU Compilation Flow
4 Python Interface and Implementation
5 Evaluation
5.1 Non-embedded Systems
5.2 Embedded System
6 Related Work
7 Conclusion
References
Towards Addressing Noise and Static Variations of Analog Computations Using Efficient Retraining
1 Introduction
2 Background and Related Work
2.1 BrainScaleS-2
2.2 Noisy Computations in the Context of Machine Learning
3 Hardware Representation
4 Training Methods and Results
5 Summary and Outlook
References
eXplainable Knowledge Discovery in Data Mining
Workshop and Tutorial on eXplainable Knowledge Discovery in Data Mining (XKDD 2021)
chPart6
Organization
XKDD 2021 Program Chairs
XKDD 2021 Program Committee
The Next Frontier: AI We Can Really Trust
1 Success in Machine Learning Enabled a New AI Spring
2 Trust and Trustworthy AI
2.1 What Is Trust?
2.2 What Is Trustworthy AI?
3 Explainability and Causability
3.1 What Is Explainable AI?
3.2 What Is Explainability?
3.3 What Is Causability?
4 Robustness
4.1 Robustness in General
4.2 Robustness in Interventional Studies
4.3 Robustness to Adversarial Attacks
5 How Can a Human-in-the-Loop Help?
6 Conclusion
References
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
1 Introduction
2 Prototypical Part Network
3 Methodology
3.1 Important Visual Characteristics
3.2 Image Modifications
3.3 Importance Scores for Image Characteristics
4 Experimental Setup
4.1 Modification Implementation
4.2 Considerations for Evaluation
5 Results and Discussion
5.1 Analysing Our Local Explanations
5.2 Analysing Our Global Explanations
5.3 Redundant Prototypes
6 Conclusion and Future Work
References
Prototypical Convolutional Neural Network for a Phrase-Based Explanation of Sentiment Classification
1 Introduction
2 Related Works
3 Prototype-Based Convolutional Neural Network for Text
3.1 Architecture of the Proposed Solution
3.2 Loss Function
3.3 Prototype Projection
3.4 Dynamic Number of Prototypes
4 Experiments
4.1 Experimental Setup
4.2 Comparison of the Proposed Model with Reference Methods
4.3 Analyzing the Impact of the Number of Prototypes
4.4 Visualization of Learned Prototypes
4.5 Explaining Text Classification with Prototypes
4.6 Examining the Fidelity of the Model
5 Final Remarks
References
Explanations for Network Embedding-Based Link Predictions
1 Introduction
2 Methods
2.1 Notation
2.2 NE-Based Link Predictions
2.3 ExplaiNE as a Generic Approach
2.4 Making ExplaiNE Scalable
2.5 ExplaiNE for CNE
2.6 ExplaiNE for Other NE Methods
3 Experiments
3.1 Quality of the ExplaiNE Approximation
3.2 Qualitative Evaluation
3.3 Quantitative Evaluation
3.4 Scalability and Runtime
4 Related Work
5 Conclusions
References
Exploring Counterfactual Explanations for Classification and Regression Trees
1 Introduction
2 Solving Counterfactual Exactly in Decision Trees
2.1 Leaf Region: Definition
2.2 Counterfactual Problem in Decision Trees
2.3 Separable Problems: A Special Case for Axis-Aligned Trees
2.4 Categorical Variables
3 Exploring Different Types of Counterfactual Explanation Questions
4 Experiments
4.1 Dataset Information
4.2 Use Case Study 1
4.3 Use Case Study 2
4.4 Use Case Study 3
5 Conclusion
References
Towards Explainable Meta-learning
1 Introduction
2 Meta-learning Frameworks
3 The Meta-OpenML100 Surrogate Model
3.1 Predictive Tasks and Their Meta-features
3.2 Landmarkers
3.3 Algorithms and Hyperparameters Space
3.4 Estimated Predictive Power of Selected Configurations
3.5 Surrogate Meta-model
4 Explanatory Analysis of Meta-OpenML100 Model
4.1 Meta-features Importance
4.2 Meta-features Interactions
4.3 Importance of Correlated Meta-features
4.4 Hyperparameters Informativeness
4.5 Robustness of Meta-data
5 Conclusions
References
How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
1 Implementing Explainability: Current State of the Literature
1.1 Understanding Stakeholder Needs
1.2 Specifying Explanation Method Properties
1.3 There is a Lack of Methodological Guidance
2 Proposed Methodology
2.1 Explanation Method Properties
2.2 Explainability Needs
2.3 Information Collection
2.4 Matching Stakeholder Needs with Explanation Method Properties
3 Ongoing and Future Research
A Explanation Method Properties (ID Cards)
A.1 Compatibility Properties
A.2 Explanation Properties
A.3 Method Usage
A.4 Process Properties
B Explainability Needs
B.1 Use Case Context
B.2 Stakeholder
B.3 Stakeholder Needs
B.4 Shared Stakeholder Constraints
C Questionnaire to Reveal Needs
C.1 Use Case Context
C.2 Stakeholder
C.3 Stakeholder Needs
C.4 Stakeholder Constraints
References
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data
1 Introduction
2 Explainable Boosting Machines
3 Missing Values
3.1 Missing Values Assumed Normal
3.2 Missing Values Imputed with the Mean
4 Confounding and Treatment Effects
4.1 Treatment Effects and Model Correctness
4.2 Discovering New Science
5 Data Drift
6 Bias and Fairness
7 Outliers
8 Discussion
References
Bias and Fairness in AI
2nd Workshop on Bias and Fairness in Artificial Intelligence (BIAS 2021)
chPart7
Organization
Workshop Co-chairs
Program Committee
Algorithmic Factors Influencing Bias in Machine Learning
1 Introduction
2 Background
2.1 Quantifying Bias
3 Factors Contributing to Underestimation
4 Underestimation on Synthetic Data
4.1 Data Description
4.2 Underestimation and Classification Bias
4.3 Impact of Irreducible Error
4.4 Impact of Regularization
4.5 Impact of Class and Feature Imbalance
5 Remediation
5.1 Adding Counterfactuals
5.2 Tuning
6 Underestimation on Real Datasets
7 Conclusions and Future Work
References
Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank
1 Introduction
2 Related Work
3 User-Centric Classification of Desiderata for Explainability Needs
3.1 Users
3.2 Building Trust
3.3 Gaining Knowledge Through Machine Learning
3.4 Model Monitoring
3.5 Actionable Insights
3.6 Fostering Explanations Through Simple Models
4 Use Cases
4.1 Centralised Securities Database
4.2 Supervisory Banking Data System
5 Conclusions
References
Robustness of Fairness: An Experimental Analysis
1 Introduction
1.1 Related Work
2 Preliminaries and Overview
3 Framework and Experimental Setup
3.1 Datasets
3.2 Metrics
3.3 Methodology
4 Results
4.1 Variance of Fairness and Performance Metrics
4.2 Direction of Unfairness
5 Conclusions and Future Work
References
Co-clustering for Fair Recommendation
1 Introduction
2 Model
2.1 The Latent Block Models
2.2 Model Proposed
3 Inference and Fair Recommendations
3.1 A Stochastic Batch Gradient Descent of the Variational Criterion
3.2 Fair Recommendations
4 Experiment on MovieLens Dataset
4.1 Experimental Protocol
4.2 Results and Discussion
5 Conclusion
A Computation of the Variational Log-Likelihood Criterion
B Clustering -parity and -fair Recommendation for Arbitrary Discrete Sensitive Attribute
C Proof of Theorem 1
D Supplemental Results for MovieLens 1M
D.1 Gender as Sensitive Attribute
D.2 Age as Sensitive Attribute
References
Learning a Fair Distance Function for Situation Testing
1 Introduction
2 Analysis of Existing Situation Testing Methods
2.1 Situation Testing - Luong et al.
2.2 Situation Testing - Zhang et al.
3 Learning a Fair Distance Measure
3.1 Theoretical Justification of the Distance Optimization Problem
4 Learning the Distances and Setting Hyperparameters
4.1 Learning the Distance Function
4.2 Selecting Neighbors from the Unprotected Group only
4.3 Setting the Number of Selected Neighbours
4.4 Setting the Threshold
5 Experiments on Simulated Data
5.1 Generation Process of Data
5.2 Experimental Setup
5.3 Discrimination Detection Without Explainable Discrimination
5.4 Discrimination Detection in the Presence of Explainable Discrimination
6 Qualitative Experiments on Real Data
6.1 Case Examples
7 Discussion and Conclusion
References
Towards Fairness Through Time
1 Introduction and Motivation
2 Related Works
3 Problem Setting
4 Experiments
4.1 C1: Evaluating Fairness Stability Through Time
4.2 C2: Providing Fairness Stability Through Time
4.3 C3: Monitoring Fairness Through Time
5 Conclusion and Future Outlooks
References
International Workshop on Active Inference
Workshop on Active Inference (IWAI 2021)
chPart8
Organization
Organizing Committee
Program Committee
Active Inference for Stochastic Control
1 Introduction
2 Stochastic Control in a Windy Grid-World
2.1 Grid-World Complexity
3 Active Inference on Finite Temporal Horizons
3.1 Generative Model
3.2 Full Observability
3.3 Partial Observability
4 Results
5 Discussion
A Results Level-1 and Level-3 (Non-stochastic Settings)
B Outcome Modalities for POMDPs
References
Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference
1 Introduction
2 Problem Statement and Background
3 Precision Learning for Fault-Tolerant Control
4 Results
5 Improving Precision Learning: A Discussion
6 Conclusions
References
On the Convergence of DEM's Linear Parameter Estimator
1 Introduction
2 Preliminaries
3 Parameter Learning as Free Energy Optimization
4 Proof of Convergence for Parameter Estimator
5 Proof of Concept: Mass-Spring-Damper System
6 Conclusion
References
Disentangling What and Where for 3D Object-Centric Representations Through Active Inference
1 Introduction
2 Method
2.1 A Generative Model for Object-Centric Perception
2.2 An Ensemble of CCNs
2.3 Classification by Minimizing Expected Free Energy
3 Experiments
3.1 The ``what'' Stream: Object Recognition
3.2 The ``where'' Stream: Implicit Pose Estimation
3.3 Embodied Agents for Improved Classification
4 Related Work
5 Discussion
6 Conclusion
A Neural Network Architecture and Training Details
B Additional experimental details
References
Rule Learning Through Active Inductive Inference
1 Introduction
2 Active Inference
2.1 Evidence Accumulating Agent
2.2 Bayesian Model Reduction
3 Grammar-Based Rule Induction
4 Experiments
5 Discussion
References
Interpreting Dynamical Systems as Bayesian Reasoners
1 Introduction
2 Definitions and Results
2.1 Technical Preliminaries
2.2 Machines and Interpretations
2.3 Consistency for Bayesian Interpretations
3 Discussion
3.1 Relation to the Free Energy Principle
A Category-Theoretic Probability and String Diagrams
A.1 Introduction to String Diagrams and Category-Theoretic Probability
A.2 The Extension to Measure Theory
A.3 Conditionals and Bayes' Theorem
B More Details About Bayesian Interpretations
B.1 Unpacking Bayesian Filtering Interpretations
B.2 More on Bayesian Filtering
B.3 Bayesian Inference Interpretations and Conjugate Priors
B.4 Unpacking Bayesian Inference Interpretations
C Details of Examples
C.1 An Interpretation of a Non-Deterministic Finite Machine
C.2 Machine Counting Occurrences of Different Observations
C.3 Machine Tracking Differences Between the Number of Occurrences of Different Observations
D Details on the Relation to the FEP
D.1 Machine
D.2 Model
D.3 Interpretation map
References
Blankets All the Way up – the Economics of Active Inference
1 Introduction
2 Expected Free Energy and Active Inference
3 Discounted Utility
4 Probability and Utility Spaces
5 Active Inference and Biases
6 A Simple Model
7 Conclusion
References
Filtered States: Active Inference, Social Media and Mental Health
1 Introduction
2 Introducing Active Inference
3 Your Brain on Social Media
4 Designing Addictive Digital Spaces
5 Conclusion
References
Ideas Worth Spreading: A Free Energy Proposal for Cumulative Cultural Dynamics
1 Introduction
2 Method
2.1 Simulating the Local Dynamics of Communication
2.2 Simulating the Global Dynamics of Cumulative Culture
3 A Generative Model of Communication
3.1 Perceptual Inference
3.2 Anticipation
3.3 Action
3.4 Perceptual Learning
4 Results
4.1 Local Dynamics of Coupled Communication
4.2 Global Dynamics of Cumulative Culture
5 Conclusion
Appendix A Generative Model Architecture, Factors and Parameters
A.1 Higher Level Hidden State Factors
A.2 Lower Level Hidden State Factors (Specify Events on a Given ‘Day’)
A.4 Lower Level Generative Model For Action
A6. Generative Process
A7. Perception
A8. Learning
A9. Initialisation of Parameters for Each Agent
References
Dream to Explore: 5-HT2a as Adaptive Temperature Parameter for Sophisticated Affective Inference
1 Introduction
2 5-HT1a Receptors
3 5-HT2a Receptors
References
Inferring in Circles: Active Inference in Continuous State Space Using Hierarchical Gaussian Filtering of Sufficient Statistics
1 Introduction
2 Filtering Sufficient Statistics with Hierarchical Gaussian Filters
3 Active Inference in Continuous State Space
4 Example Simulation
5 Discussion
References
On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference
1 Introduction
2 Stochastic Shortest Path MDP
3 Solving an SSP MDP as Probabilistic Inference
3.1 Definitions
3.2 Mixture of Finite MDPs
3.3 Computing an Optimal Policy
4 Connections Between the Two Views
4.1 Exact Relationship Between Policy Iteration and Planning as Probabilistic Inference
4.2 World States vs Temporal States
4.3 Policies, Plans and Probabilistic Plans
5 Discussion
A Appendix: Illustrations
References
Habitual and Reflective Control in Hierarchical Predictive Coding
1 Introduction
2 Hierarchical Predictive Coding (HPC)
3 Methods
4 Results
5 Discussion
A Network parameters
References
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
1 Introduction
2 Deep Active Inference Model
3 Experimental Setup
4 Results
5 Discussion
A Model Parameters
B DQN: Policy Network
C VAE
D Derivations
E Average Reward Over 100 Episodes
References
Robot Localization and Navigation Through Predictive Processing Using LiDAR
1 Introduction
2 Methods
2.1 Robot
2.2 Localization
2.3 Predicting the Observations
2.4 Navigation
3 Results
3.1 Localization and Estimation
3.2 Navigation
4 Conclusions
References
Sensorimotor Visual Perception on Embodied System Using Free Energy Principle
1 Introduction
2 Sensorimotor Visual Perception
3 Free Energy Principle
4 Embodied System for Sensorimotor Visual Perception
4.1 Perceptual Inference
4.2 Active Inference
5 Evaluation and Discussion
6 Conclusion
Appendix
References
Author Index