DOI:10.1016/j.knosys.2019.105048 - Corpus ID: 204092079
Gated Mixture Variational Autoencoders for Value Added Tax audit case selection
@article{Kleanthous2020GatedMV, title={Gated Mixture Variational Autoencoders for Value Added Tax audit case selection}, author={Christos Kleanthous and Sotirios P. Chatzis}, journal={Knowl. Based Syst.}, year={2020}, volume={188}, url={https://api.semanticscholar.org/CorpusID:204092079} }
- Christos Kleanthous, S. Chatzis
- Published in Knowledge-Based Systems 1 January 2020
- Computer Science, Business
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21 References
Asymmetric deep generative models
- Harris PartaouridesS. Chatzis
- Computer Science, Mathematics
- 2017
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach
- Daniel de RouxBoris PérezA. MorenoMaría-Del-Pilar VillamilCésar Figueroa
- Computer Science, Business
- 2018
The ability of the model to identify under-reporting taxpayers on real tax payment declarations is demonstrated, reducing the number of potential fraudulent tax payers to audit and increasing the operational efficiency in the tax supervision process without needing historic labeled data.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
- John C. DuchiElad HazanY. Singer
- Computer Science, Mathematics
- 2011
This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight.
Dropout: a simple way to prevent neural networks from overfitting
- Nitish SrivastavaGeoffrey E. HintonA. KrizhevskyI. SutskeverR. Salakhutdinov
- Computer Science
- 2014
It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Manifold learning techniques for unsupervised anomaly detection
- C. OlsonK. JuddJ. Nichols
- Computer Science, Mathematics
- 2018
Signal Modeling and Classification Using a Robust Latent Space Model Based on $t$ Distributions
- S. ChatzisD. KosmopoulosT. Varvarigou
- Computer Science
- 2008
A Bayesian approach to factor analysis modeling based on Student's-t distributions is developed, which provides an efficient and more robust alternative to EM-based methods, resolving their singularity and overfitting proneness problems, while allowing for the automatic determination of the optimal model size.
Deep Learning
- Yann LeCunYoshua BengioGeoffrey E. Hinton
- Computer Science
- 2015
Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
...
...
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