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Found 9 papers, 2 papers with code
Speech earthquakes: scaling and universality in human voice
We first show that during speech the energy is unevenly released and power-law distributed, reporting a universal robust Gutenberg-Richter-like law in speech.
Sequential visibility-graph motifs
Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science.
Emergence of linguistic laws in human voice
This means that inferences of statistical patterns of language in acoustics are biased by the arbitrary, language-dependent segmentation of the signal, and virtually precludes the possibility of making comparative studies between human voice and other animal communication systems.
Visibility graphs for image processing
The family of image visibility graphs (IVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs.
Effect of antipsychotics on community structure in functional brain networks
This suggests that Aripiprazole has a significant and measurable effect on community structure in healthy individuals but not in individuals who are diagnosed with schizophrenia.
Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce
Knowing if a user is a buyer vs window shopper solely based on clickstream data is of crucial importance for ecommerce platforms seeking to implement real-time accurate NBA (next best action) policies.
Predicting e-commerce customer conversion from minimal temporal patterns on symbolized clickstream trajectories
Knowing if a user is a buyer or window shopper solely based on clickstream data is of crucial importance for e-commerce platforms seeking to implement real-time accurate NBA (next best action) policies.
An effective theory of collective deep learning
Unraveling the emergence of collective learning in systems of coupled artificial neural networks points to broader implications for machine learning, neuroscience, and society.
Dynamical stability and chaos in artificial neural network trajectories along training
The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network's prediction, when confronted with a learning task.
