Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.

Inspired by "predictive coding" - a theory in neuroscience, we develop a bidirectional and dynamic neural network with local recurrent processing, namely

Dimensionality reduction is a fundamental approach to compression of complex, large-scale data sets, either for visualization or for pre-processing before application of supervised approaches.

Time series classification using deep neural networks, such as convolutional neural networks (CNN), operate on the spectral decomposition of the time series computed using a preprocessing step.

Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as

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Current generative models are often built on local convolutions and overlook the key challenges (e.g.

Hence, the number of chips generated per image during training adaptively changes based on the scene complexity.

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