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Found 6 papers, 1 papers with code
Opinion Dynamics in Financial Markets via Random Networks
We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model.
Independent Approximates enable closed-form estimation of heavy-tailed distributions
A new statistical estimation method, Independent Approximates (IAs), is defined and proven to enable closed-form estimation of the parameters of heavy-tailed distributions.
Methodology Information Theory Information Theory Data Analysis, Statistics and Probability 62F10
Use of Student's t-Distribution for the Latent Layer in a Coupled Variational Autoencoder
The generalized mean of the generated image's likelihood is used to measure the performance of the algorithm's decisiveness, accuracy, and robustness.
Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks
We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset.
Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
We analyze the histogram of the likelihoods of the input images using the generalized mean, which measures the model's accuracy as a function of the relative risk.
Probabilistic graphs using coupled random variables
Neural network design has utilized flexible nonlinear processes which can mimic biological systems, but has suffered from a lack of traceability in the resulting network.
