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Found 15 papers, 9 papers with code
Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru
A higher travel costs significantly reduce the predicted probability of bus usage compared to other modes (a $0. 66\%$ and $0. 34\%$ reduction using Random Forests and XGBoost model for $10\%$ increase in travel cost).
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks
Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices.
Permutation Decision Trees
Unlike Shannon entropy and Gini impurity, structural impurity based on ETC is able to capture order dependencies in the data, thus obtaining potentially different decision trees for different permutations of the same data instances (Permutation Decision Trees).
Granger Causality for Compressively Sensed Sparse Signals
In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically Circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger Causality.
Neurochaos Feature Transformation and Classification for Imbalanced Learning
Human brain effortlessly learns from imbalanced data.
Cause-Effect Preservation and Classification using Neurochaos Learning
Discovering cause-effect from observational data is an important but challenging problem in science and engineering.
Learning Generalized Causal Structure in Time-series
The science of causality explains/determines 'cause-effect' relationship between the entities of a system by providing mathematical tools for the purpose.
Fairly Constricted Multi-Objective Particle Swarm Optimization
It has been well documented that the use of exponentially-averaged momentum (EM) in particle swarm optimization (PSO) is advantageous over the vanilla PSO algorithm.
When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning
Inspired by the chaotic firing of neurons and the constructive role of noise in neuronal models, we for the first time connect chaos, noise and learning.
Causal Discovery using Compression-Complexity Measures
Lastly, we present two unique applications of the proposed models for causal inference directly from pairs of genome sequences belonging to the SARS-CoV-2 virus.
A Neurochaos Learning Architecture for Genome Classification
Here, we propose a Neurochaos Learning (NL) architecture, where the neurons used to extract features from data are 1D chaotic maps.
ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification
Inspired by chaotic firing of neurons in the brain, we propose ChaosNet -- a novel chaos based artificial neural network architecture for classification tasks.
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets
We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classification of exoplanets.
A Novel Chaos Theory Inspired Neuronal Architecture
This work highlights the effectiveness of chaos and its properties for learning and paves the way for chaos-inspired neuronal architectures by closely mimicking the chaotic nature of neurons in the brain.
Classification of Periodic, Chaotic and Random Sequences using NSRPS Complexity Measure
For such short data lengths, methods which use entropy measure and traditional lossless compression algorithm like LZ77 [A. Lempel and J. Ziv, IEEE Trans.
Chaotic Dynamics
