Search Results for author:
Found 11 papers, 5 papers with code
Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks
Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions.
Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications
Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings.
Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets
We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with auxiliary data in the form of metadata associated with the input images, are combined using a meta-learner.
Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics
Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques.
A Multilabel Classification Framework for Approximate Nearest Neighbor Search
Both supervised and unsupervised machine learning algorithms have been used to learn partition-based index structures for approximate nearest neighbor (ANN) search.
Minimum Description Length Revisited
This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition.
Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search
Therefore, we propose an algorithm for automatically tuning the hyperparameters of indexing methods based on randomized space-partitioning trees.
Learning non-parametric Markov networks with mutual information
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables.
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model.
Fast k-NN search
The method is straightforward to implement using sparse projections which leads to a reduced memory footprint and fast index construction.
Bayesian Properties of Normalized Maximum Likelihood and its Fast Computation
The normalized maximized likelihood (NML) provides the minimax regret solution in universal data compression, gambling, and prediction, and it plays an essential role in the minimum description length (MDL) method of statistical modeling and estimation.
