Search Results for author:
Found 9 papers, 3 papers with code
Automatic Feature Extraction for Heartbeat Anomaly Detection
We focus on automatic feature extraction for raw audio heartbeat sounds, aimed at anomaly detection applications in healthcare.
Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Manifold
In a non-parametric formalism, we consider the full set of positive probability functions on a finite sample space, and we provide a specific expression for the tangent and cotangent spaces over the statistical manifold, in terms of a Hilbert bundle structure that we call the Statistical Bundle.
Natural Reweighted Wake-Sleep
These models are commonly trained using a two-step optimization algorithm called Wake-Sleep (WS) and more recently by improved versions, such as Reweighted Wake-Sleep (RWS) and Bidirectional Helmholtz Machines (BiHM).
Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations
In presence of an additional dataset of unlabelled data containing also anomalies, the task can be framed as a semi-supervised task with negative and unlabelled sample points.
Parameters Estimation from the 21 cm signal using Variational Inference
Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA) and Square Kilometre Array (SKA) are intended to measure the 21cm signal over a wide range of redshifts, representing an incredible opportunity in advancing our understanding about the nature of cosmic Reionization.
Natural Alpha Embeddings
Learning an embedding for a large collection of items is a popular approach to overcome the computational limitations associated to one-hot encodings.
Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks
In the second part of the paper, we present a guide to the training and calibration of a successful multi-channel BNN for the CMB temperature and polarization map.
Variational autoencoders trained with q-deformed lower bounds
In training, they exploit the power of variational inference, by optimizing a lower bound on the model evidence.
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
In this paper we propose two novel bounds for the log-likelihood based on Kullback-Leibler and the R\'{e}nyi divergences, which can be used for variational inference and in particular for the training of Variational AutoEncoders.
