1
Deep Reinforcement Learning
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ExplAIn Yourself! Transparency for Positive UX in Autonomous Driving
3
Machine Learning with Applications
4
AI System Engineering - Key Challenges and Lessons Learned
5
Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine
6
Explainable AI Framework for Multivariate Hydrochemical Time Series
7
A Survey on the Explainability of Supervised Machine Learning
8
Guidelines for Quality Assurance of Machine Learning-based Artificial Intelligence
9
Survey into predictive key performance indicator analysis from data mining perspective
10
Legal requirements on explainability in machine learning
12
Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions
13
Machine learning applications
14
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
15
Preliminary Systematic Literature Review of Machine Learning System Development Process
16
InterpretML: A Unified Framework for Machine Learning Interpretability
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Serving Machine Learning Workloads in Resource Constrained Environments: a Serverless Deployment Example
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Understanding Development Process of Machine Learning Systems: Challenges and Solutions
19
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
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How to Achieve Explainability and Transparency in Human AI Interaction
21
Unit Testing Data with Deequ
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Machine Learning Testing: Survey, Landscapes and Horizons
23
Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model
24
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis
25
Unreproducible Research is Reproducible
26
Software Engineering for Machine Learning: A Case Study
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Data Infrastructure for Machine Learning
28
Data Shapley: Equitable Valuation of Data for Machine Learning
30
Unmasking Clever Hans predictors and assessing what machines really learn
31
Metamorphic testing of driverless cars
32
Machine Learning at Facebook: Understanding Inference at the Edge
33
Quantifying Interpretability and Trust in Machine Learning Systems
34
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
35
Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps
36
Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems
37
Applying spatial intelligence for decision support systems
38
Interpretability and Reproducability in Production Machine Learning Applications
39
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
40
"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data
41
Deep Reinforcement Learning
42
Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems
43
Towards Enterprise-Ready AI Deployments Minimizing the Risk of Consuming AI Models in Business Applications
44
Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI
45
iNNvestigate neural networks!
46
Training CNNs from Synthetic Data for Part Handling in Industrial Environments
47
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
48
The Measure and Mismeasure of Fairness
49
Feature selection in machine learning: A new perspective
50
A Practical Taxonomy of Reproducibility for Machine Learning Research
51
[Journal First] Data Scientists in Software Teams: State of the Art and Challenges
52
An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning
53
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
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The Lottery Ticket Hypothesis: Training Pruned Neural Networks
55
Winner's Curse? On Pace, Progress, and Empirical Rigor
56
Deep Visual Domain Adaptation: A Survey
57
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
58
Multiple Imputation: A Review of Practical and Theoretical Findings
59
Fairness in Machine Learning: Lessons from Political Philosophy
60
The ML test score: A rubric for ML production readiness and technical debt reduction
61
A Survey of Model Compression and Acceleration for Deep Neural Networks
62
Deep Reinforcement Learning that Matters
63
DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars
64
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
65
Machine Teaching: A New Paradigm for Building Machine Learning Systems
66
On the State of the Art of Evaluation in Neural Language Models
67
A Unified Approach to Interpreting Model Predictions
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Data Management Challenges in Production Machine Learning
69
Deep Learning in Automotive Software
70
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
71
Dermatologist-level classification of skin cancer with deep neural networks
72
A survey: Control plane scalability issues and approaches in Software-Defined Networking (SDN)
73
"What is relevant in a text document?": An interpretable machine learning approach
74
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
75
Neural Architecture Search with Reinforcement Learning
76
Autonomous Driving in the Real World: Experiences with Tesla Autopilot and Summon
77
Responsibility, Autonomy and Accountability: Legal Liability for Machine Learning
78
Understanding Data Augmentation for Classification: When to Warp?
79
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
80
MVP Explained: A Systematic Mapping Study on the Definitions of Minimal Viable Product
81
ModelDB: a system for machine learning model management
82
Recurrent Neural Networks for Multivariate Time Series with Missing Values
83
Debugging Machine Learning Tasks
84
Safe learning of regions of attraction for uncertain, nonlinear systems with Gaussian processes
85
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
86
Hidden Technical Debt in Machine Learning Systems
87
Efficient and Robust Automated Machine Learning
88
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
89
What Is Machine Learning
90
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
91
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data
92
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
93
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Implementation of Six Sigma to Reduce Cost of Quality: A Case Study of Automobile Sector
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Explaining and Harnessing Adversarial Examples
96
Machine learning applications in cancer prognosis and prediction
97
How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective
98
How transferable are features in deep neural networks?
99
Semi-supervised Learning with Deep Generative Models
100
Cloud Computing Patterns: Fundamentals to Design, Build, and Manage Cloud Applications
101
A REVIEW OF FEATURE SELECTION TECHNIQUES IN STRUCTURE LEARNING
102
Safe Automotive Software
103
Multiple imputation by chained equations: what is it and how does it work?
104
Multiple imputation using chained equations: Issues and guidance for practice
105
A survey of data mining and knowledge discovery process models and methodologies
106
Why Does Unsupervised Pre-training Help Deep Learning?
107
Ensemble-based classifiers
108
How to Explain Individual Classification Decisions
109
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
110
Extending CRISP-DM to incorporate temporal data mining of multidimensional medical data streams: A neonatal intensive care unit case study
111
Matrix Factorization Techniques for Recommender Systems
112
Toward data mining engineering: A software engineering approach
113
On Relevant Dimensions in Kernel Feature Spaces
114
Covariate Shift Adaptation by Importance Weighted Cross Validation
115
Future trends in data mining
116
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
117
A survey of Knowledge Discovery and Data Mining process models
118
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
119
ANN quality diagnostic models for packaging manufacturing: an industrial data mining case study
120
A study of the behavior of several methods for balancing machine learning training data
121
Survey of multi-objective optimization methods for engineering
122
Understanding Digital Signal Processing (2nd Edition)
123
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
125
Ensembling neural networks: Many could be better than all
126
Selection bias in gene extraction on the basis of microarray gene-expression data
127
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
128
An introduction to kernel-based learning algorithms
129
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites
131
Predictive modeling in automotive direct marketing: tools, experiences and open issues
132
Analysing Warranty Claims of Automobiles; An Application Description Following the CRISP-DM Data Mining Process
133
Popular Ensemble Methods: An Empirical Study
134
A Primer on Wavelets and Their Scientific Applications
135
Selection of Relevant Features and Examples in Machine Learning
136
Kernel Principal Component Analysis
137
Understanding Digital Signal Processing
138
The Lack of A Priori Distinctions Between Learning Algorithms
139
Active Learning with Statistical Models
140
Learning internal representations by error propagation
141
The Engineering Approach
142
Survey Analysis: AI and ML Development Strategies, Motivators and Adoption Challenges, 2019
143
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
144
The Machine Learning Reproducibility Checklist
145
Continuous Deployment of Machine Learning Pipelines
146
DMME: Data mining methodology for engineering applications – a holistic extension to the CRISP-DM model
147
Automated Machine Learning - Methods, Systems, Challenges
148
VDA), V. FMEA Handbook-Failure Mode and Effects Analysis
149
Automated Machine Learning: Methods, Systems, Challenges
150
Artificial Intelligence: Localization Winners, Losers, Heroes, Spectators, and You
151
From Predictive Methods to Missing Data Imputation: An Optimization Approach
152
Curse of Dimensionality
153
Megaman: Scalable Manifold Learning in Python
155
Autonomy and Accountability: legal liability for machine learning. Queen Mary School of Law Legal Studies Research Paper
156
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
157
CRISP Data Mining Methodology Extension for Medical Domain
158
Europeen. EN ISO 9001:2015 Quality Management Systems-Requirements
159
A survey on feature selection methods
160
Cloud Computing Patterns
161
Semi-Supervised Learning
163
Learning Feature Representations with K-Means
164
Review of feature selection techniques in bioinformatics
165
APU FMEA Validation and Its Application to Fault Identification
166
Crisp-dm0 : A method to extend crisp-dm to support null hypothesis driven confirma-tory data mining
167
industrial science and engineering
169
Gaussian Processes for Global Optimization
170
Specializing CRISP-DM for Evidence Mining
171
Pattern recognition and machine learning, 5th Edition; Information science and statistics
172
Predicting the Price of Used Cars using Machine Learning Techniques
173
An Introduction to Variable and Feature Selection
174
SMOTE: Synthetic Minority Over-sampling Technique
175
Neural Networks: Tricks of the Trade
176
CRISP-DM 1.0: Step-by-step data mining guide
177
CRISP-DM: Towards a Standard Process Model for Data Mining
178
Using the Nyström Method to Speed Up Kernel Machines
179
The Nature of Statistical Learning Theory
180
The CRISP-DM Model: The New Blueprint for Data Mining
181
Neural Network Classification and Prior Class Probabilities
182
The Lack of A Priori Distinctions Between Learning Algorithms
183
An introduction to VAR
184
IEEE Standard for developing software life cycle processes
186
The Lottery Ticket Hypothesis
187
Artificial Intelligence (AI) -Assessment of the Robustness of Neural Networks
188
Safety First For Automated Driving
