Search Results for author:
Found 8 papers, 2 papers with code
MANDERA: Malicious Node Detection in Federated Learning via Ranking
Federated learning is a distributed learning paradigm which seeks to preserve the privacy of each participating node's data.
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Poisson Processes
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in Poisson processes using projections into lower-dimensional space.
A Deep Architecture for Log-Linear Models
We present a novel perspective on deep learning architectures using a partial order structure, which is naturally incorporated into the information geometric formulation of the log-linear model.
Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds
Learning of the model is achieved via convex optimization, thanks to the dually flat statistical manifold generated by the log-linear model.
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower dimensional projections.
Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training
On the other hand, there is smaller fraction of examples that contain all modalities (\emph{paired} data) and furthermore each modality is high dimensional when compared to number of datapoints.
Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS).
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
However, it is well known that increasing the number of parameters also increases the complexity of the model which leads to a bias-variance trade-off.
