1
An Empirical Study on Class Rarity in Big Data
2
Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud Detection
3
A survey on addressing high-class imbalance in big data
4
Predicting Hospital Readmission via Cost-Sensitive Deep Learning
5
Big Data fraud detection using multiple medicare data sources
6
The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data
7
Recent Advances in Deep Learning: An Overview
8
Classification of Rare Building Change Using CNN with Multi-Class Focal Loss
9
Image classification with category centers in class imbalance situation
10
Imbalanced Deep Learning by Minority Class Incremental Rectification
11
Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification
12
A comparative study of open source deep learning frameworks
13
Facial action recognition using very deep networks for highly imbalanced class distribution
14
Automatic differentiation in PyTorch
15
A systematic study of the class imbalance problem in convolutional neural networks
16
Focal Loss for Dense Object Detection
17
Deep Over-sampling Framework for Classifying Imbalanced Data
18
EmotioNet Challenge: Recognition of facial expressions of emotion in the wild
19
Beyond Skip Connections: Top-Down Modulation for Object Detection
20
Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques
21
An Incorporation of Artificial Intelligence Capabilities in Cloud Computing
22
Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning
23
Densely Connected Convolutional Networks
24
Training deep neural networks on imbalanced data sets
25
Training cost-sensitive Deep Belief Networks on imbalance data problems
26
Learning Deep Representation for Imbalanced Classification
27
Deep Learning without Poor Local Minima
28
Learning from imbalanced data: open challenges and future directions
29
Training Region-Based Object Detectors with Online Hard Example Mining
30
Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization
31
Deep Residual Learning for Image Recognition
32
SSD: Single Shot MultiBox Detector
33
Rethinking the Inception Architecture for Computer Vision
34
Cost-Aware Pre-Training for Multiclass Cost-Sensitive Deep Learning
35
WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification
36
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
37
You Only Look Once: Unified, Real-Time Object Detection
38
Deep domain adaptation for describing people based on fine-grained clothing attributes
39
Brain tumor segmentation with Deep Neural Networks
40
FaceNet: A unified embedding for face recognition and clustering
41
Deep learning applications and challenges in big data analytics
42
Striving for Simplicity: The All Convolutional Net
43
The Loss Surfaces of Multilayer Networks
44
Deep Learning Face Attributes in the Wild
45
cuDNN: Efficient Primitives for Deep Learning
46
Very Deep Convolutional Networks for Large-Scale Image Recognition
47
ImageNet Large Scale Visual Recognition Challenge
48
Big Data Analytics: A Literature Review Paper
49
Perspectives on Big Data Analysis
50
Deep Learning Face Representation by Joint Identification-Verification
51
Microsoft COCO: Common Objects in Context
52
Deep learning in neural networks: An overview
53
PANDA: Pose Aligned Networks for Deep Attribute Modeling
54
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
55
ImageNet classification with deep convolutional neural networks
56
SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory
57
Multiclass Imbalance Problems: Analysis and Potential Solutions
58
Effective detection of sophisticated online banking fraud on extremely imbalanced data
59
Automated annotation of coral reef survey images
60
An Analysis of Single-Layer Networks in Unsupervised Feature Learning
61
Contour Detection and Hierarchical Image Segmentation
62
A Study on the Relationships of Classifier Performance Metrics
63
Learning from Imbalanced Data
65
ImageNet: A large-scale hierarchical image database
66
Reconstructing the giant: On the importance of rigour in documenting the literature search process
67
Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem
68
Exploratory Undersampling for Class-Imbalance Learning
69
KEEL: a software tool to assess evolutionary algorithms for data mining problems
70
Boosted Classification Trees and Class Probability/Quantile Estimation
71
Cost-sensitive boosting for classification of imbalanced data
72
A Metaheuristic Controller for Cooperative Manipulators
73
Experimental perspectives on learning from imbalanced data
74
Greedy Layer-Wise Training of Deep Networks
75
Extreme learning machine: Theory and applications
76
A Fast Learning Algorithm for Deep Belief Nets
77
The relationship between Precision-Recall and ROC curves
78
Data mining for improved cardiac care
79
Combating imbalance in network intrusion datasets
80
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
81
The Imbalanced Training Sample Problem: Under or over Sampling?
82
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
83
Class imbalances versus small disjuncts
84
Mining with rarity: a unifying framework
85
Editorial: special issue on learning from imbalanced data sets
86
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
87
The Foundations of Cost-Sensitive Learning
88
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
89
An improved algorithm for neural network classification of imbalanced training sets
90
Backpropagation Applied to Handwritten Zip Code Recognition
91
Concept Learning and the Problem of Small Disjuncts
92
Learning representations by back-propagating errors
93
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
94
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
95
Deep learning for neural networks
97
The Impact of Imbalanced Training Data for Convolutional Neural Networks
98
Reading Digits in Natural Images with Unsupervised Feature Learning
99
Discovering Binary Codes for Documents by Learning Deep Generative Models
100
SMOTE: Synthetic Minority Over-sampling Technique
101
Gradient-based learning applied to document recognition
102
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection
103
Facial action coding system: a technique for the measurement of facial movement
104
Two Modifications of CNN
105
VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
