research
∙
03/17/2023
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
Self-supervised learning is a central component in recent approaches to ...
share
research
∙
03/16/2023
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Distance-based classification is frequently used in transductive few-sho...
research
∙
01/21/2023
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
The Cauchy-Schwarz (CS) divergence was developed by PrÃncipe et al. in 2...
research
∙
10/15/2022
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
The need for interpretable models has fostered the development of self-e...
research
∙
07/11/2022
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Deep learning-based approaches for content-based image retrieval (CBIR) ...
research
∙
05/31/2022
Principle of Relevant Information for Graph Sparsification
Graph sparsification aims to reduce the number of edges of a graph while...
research
∙
05/18/2022
The Kernelized Taylor Diagram
This paper presents the kernelized Taylor diagram, a graphical framework...
research
∙
05/07/2022
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
Developing a new diagnostic models based on the underlying biological me...
research
∙
03/17/2022
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
The lack of labeled data is a key challenge for learning useful represen...
research
∙
03/03/2022
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Recent work has shown that label-efficient few-shot learning through sel...
research
∙
01/10/2022
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
The recent trend of integrating multi-source Chest X-Ray datasets to imp...
research
∙
12/19/2021
RELAX: Representation Learning Explainability
Despite the significant improvements that representation learning via se...
research
∙
11/06/2021
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
Multi-modality data is becoming readily available in remote sensing (RS)...
research
∙
10/09/2021
Discriminative Multimodal Learning via Conditional Priors in Generative Models
Deep generative models with latent variables have been used lately to le...
research
∙
08/27/2021
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Current machine learning models have shown high efficiency in solving a ...
research
∙
07/07/2021
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit
The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients ...
research
∙
03/13/2021
Reconsidering Representation Alignment for Multi-view Clustering
Aligning distributions of view representations is a core component of to...
research
∙
01/25/2021
Measuring Dependence with Matrix-based Entropy Functional
Measuring the dependence of data plays a central role in statistics and ...
research
∙
12/07/2020
Joint Optimization of an Autoencoder for Clustering and Embedding
Incorporating k-means-like clustering techniques into (deep) autoencoder...
research
∙
09/03/2020
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation
Capturing global contextual representations by exploiting long-range pix...
research
∙
04/21/2020
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
We propose a novel architecture called the Multi-view Self-Constructing ...
research
∙
04/21/2020
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in dev...
research
∙
04/15/2020
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
Image translation with convolutional autoencoders has recently been used...
research
∙
03/15/2020
Self-Constructing Graph Convolutional Networks for Semantic Labeling
Graph Neural Networks (GNNs) have received increasing attention in many ...
research
∙
02/27/2020
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
A large fraction of the electronic health records (EHRs) consists of cli...
research
∙
01/20/2020
Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives
In this paper we develop a new model for deep image clustering, using co...
research
∙
01/13/2020
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been u...
research
∙
12/19/2019
LS-Net: Fast Single-Shot Line-Segment Detector
In low-altitude Unmanned Aerial Vehicle (UAV) flights, power lines are c...
research
∙
09/25/2019
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
Analyzing deep neural networks (DNNs) via information plane (IP) theory ...
research
∙
09/07/2019
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network
It is important, but challenging, for the forest industry to accurately ...
research
∙
08/30/2019
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images
We propose a network for semantic mapping called the Dense Dilated Convo...
research
∙
07/10/2019
Time series cluster kernels to exploit informative missingness and incomplete label information
The time series cluster kernel (TCK) provides a powerful tool for analys...
research
∙
03/14/2019
Learning Latent Representations of Bank Customers With The Variational Autoencoder
Learning data representations that reflect the customers' creditworthine...
research
∙
02/20/2019
Noisy multi-label semi-supervised dimensionality reduction
Noisy labeled data represent a rich source of information that often are...
research
∙
02/13/2019
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning rep...
research
∙
11/29/2018
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series
The task of clustering unlabeled time series and sequences entails a par...
research
∙
08/23/2018
Multivariate Extension of Matrix-based Renyi's α-order Entropy Functional
The matrix-based Renyi's α-order entropy functional was recently introdu...
research
∙
07/19/2018
The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the i...
research
∙
07/16/2018
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps
Convolutional Neural Networks (CNNs) are propelling advances in a range ...
research
∙
06/07/2018
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder
Identifying customer segments in retail banking portfolios with differen...
research
∙
05/09/2018
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
Learning compressed representations of multivariate time series (MTS) fa...
research
∙
04/18/2018
Understanding Convolutional Neural Network Training with Information Theory
Using information theoretic concepts to understand and explore the inner...
research
∙
03/21/2018
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
A large fraction of the electronic health records consists of clinical m...
research
∙
03/21/2018
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a...
research
∙
11/17/2017
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
Clinical measurements that can be represented as time series constitute ...
research
∙
11/17/2017
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate ti...
research
∙
11/17/2017
Bidirectional deep-reservoir echo state networks
We propose a deep architecture for the classification of multivariate ti...
research
∙
11/17/2017
Bidirectional deep echo state networks
In this work we propose a deep architecture for the classification of mu...
research
∙
10/20/2017
Learning compressed representations of blood samples time series with missing data
Clinical measurements collected over time are naturally represented as m...
research
∙
09/21/2017