research
∙
04/28/2023
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Self-training based semi-supervised learning algorithms have enabled the...
share
research
∙
03/07/2023
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
Given a huge, online stream of time-evolving events with multiple attrib...
research
∙
11/25/2022
Toward Unlimited Self-Learning Monte Carlo with Annealing Process Using VAE's Implicit Isometricity
Self-learning Monte Carlo (SLMC) methods are recently proposed to accele...
research
∙
05/07/2021
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
Although neural networks are capable of reaching astonishing performance...
research
∙
09/30/2019
ATOL: Measure Vectorisation for Automatic Topologically-Oriented Learning
Robust topological information commonly comes in the form of a set of pe...
research
∙
09/30/2019
ATOL: Automatic Topologically-Oriented Learning
There are abundant cases for using Topological Data Analysis (TDA) in a ...
research
∙
06/13/2019
Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks
This paper presents an innovative and generic deep learning approach to ...
research
∙
04/20/2019
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Persistence diagrams, the most common descriptors of Topological Data An...
research
∙
04/20/2019
PersLay: A Simple and Versatile Neural Network Layer for Persistence Diagrams
Persistence diagrams, a key descriptor from Topological Data Analysis, e...
research
∙
04/20/2019
A General Neural Network Architecture for Persistence Diagrams and Graph Classification
Graph classification is a difficult problem that has drawn a lot of atte...
research
∙
11/12/2018
