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06/19/2023
Supervised Auto-Encoding Twin-Bottleneck Hashing
Deep hashing has shown to be a complexity-efficient solution for the App...
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01/25/2022
Cold Start Active Learning Strategies in the Context of Imbalanced Classification
We present novel active learning strategies dedicated to providing a sol...
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01/17/2022
H E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin Eosin regression
Computational pathology is a domain that aims to develop algorithms to a...
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06/01/2021
Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features
Cross-modal retrieval is an important functionality in modern search eng...
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12/07/2020
Computing flood probabilities using Twitter: application to the Houston urban area during Harvey
In this paper, we investigate the conversion of a Twitter corpus into ge...
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08/12/2020
Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
Despite the evolution of deep-learning-based visual-textual processing s...
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03/16/2020
Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs
Co-occurence networks can be adequately modeled by hyper-bag-graphs (hb-...
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05/28/2019
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
Traditional verbatim browsers give back information in a linear way acco...
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05/27/2019
Learning by stochastic serializations
Complex structures are typical in machine learning. Tailoring learning a...
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03/12/2019
Extracting localized information from a Twitter corpus for flood prevention
In this paper, we discuss the collection of a corpus associated to tropi...
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09/01/2018
Exchange-Based Diffusion in Hb-Graphs: Highlighting Complex Relationships
Most networks tend to show complex and multiple relationships between en...
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09/01/2018
Hypergraph Modeling and Visualisation of Complex Co-occurence Networks
Finding inherent or processed links within a dataset allows to discover ...
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09/01/2018
On Adjacency and e-Adjacency in General Hypergraphs: Towards a New e-Adjacency Tensor
In graphs, the concept of adjacency is clearly defined: it is a pairwise...
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05/30/2018
Adjacency and Tensor Representation in General Hypergraphs.Part 2: Multisets, Hb-graphs and Related e-adjacency Tensors
HyperBagGraphs (hb-graphs as short) extend hypergraphs by allowing the h...
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05/16/2018
Structured nonlinear variable selection
We investigate structured sparsity methods for variable selection in reg...
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04/19/2018
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regres...
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12/21/2017
On Adjacency and e-adjacency in General Hypergraphs: Towards an e-adjacency Tensor
Adjacency between two vertices in graphs or hypergraphs is a pairwise re...
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12/21/2017
Adjacency Matrix and Co-occurrence Tensor of General Hypergraphs: Two Well Separated Notions
Adjacency and co-occurrence are two well separated notions: even if they...
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10/02/2017
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks
We present a new method for forecasting systems of multiple interrelated...
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06/27/2017
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Traditional linear methods for forecasting multivariate time series are ...
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01/14/2017
On Hölder projective divergences
We describe a framework to build distances by measuring the tightness of...
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07/07/2015
Learning Leading Indicators for Time Series Predictions
We consider the problem of learning models for forecasting multiple time...
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05/12/2014
