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Found 5 papers, 0 papers with code
Chaos Theory and Adversarial Robustness
Neural networks, being susceptible to adversarial attacks, should face a strict level of scrutiny before being deployed in critical or adversarial applications.
Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms
Ordinary Deep Learning models require having the dimension of their outputs determined by a human practitioner prior to training and operation.
Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty
Using Machine Learning systems in the real world can often be problematic, with inexplicable black-box models, the assumed certainty of imperfect measurements, or providing a single classification instead of a probability distribution.
DOODLER: Determining Out-Of-Distribution Likelihood from Encoder Reconstructions
Deep Learning models possess two key traits that, in combination, make their use in the real world a risky prospect.
Unsupervised Learning for Target Tracking and Background Subtraction in Satellite Imagery
This paper describes an unsupervised machine learning methodology capable of target tracking and background suppression via a novel dual-model approach.
