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Classifying Imbalanced Data with AUM Loss
Joseph R. Barr
, Toby D. Hocking
, Garinn Morton
, Tyler Thatcher
, Peter Shaw
Informatics, Computing, and Cyber Systems, School of
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Computer Science
Compressed Data
100%
Feed Forward Neural Networks
100%
Feedforward Neural Network
100%
Imbalanced Data
100%
Intermediate Step
100%
Label Function
100%
Long Short-Term Memory Networks
100%
Minority Class
100%
Open Source Project
100%
Simulation Technique
100%
Source Codes
100%
Sparsity
100%
Keyphrases
Byte Pair Encoding
50%
Compress
50%
Compressed Data
50%
Cost Function
100%
Excellent Performance
50%
Extremely Sparse
50%
Feedforward Neural Network
50%
Heuristic-based
50%
Imbalanced Data
100%
Labeled Data
50%
Long Short-term Memory Network
50%
Minority Class
50%
Open Source Project
50%
Out-of-vocabulary
50%
Simulation Techniques
50%
Source Code
50%
Sparsity
50%
Token
50%
Upsampling
100%
Vulnerability
50%