23 citations · 91 across the 21 of their papers we have counts for
22 papers
Supervised Auto-Encoding Twin-Bottleneck Hashing
Yuan Chen, Stéphane Marchand-Maillet
Deep hashing has shown to be a complexity-efficient solution for the Approximate Nearest Neighbor search problem in high dimensional space. Many methods usually build the loss func…
Cold Start Active Learning Strategies in the Context of Imbalanced Classification
Etienne Brangbour, Pierrick Bruneau, Thomas Tamisier +1
We present novel active learning strategies dedicated to providing a solution to the cold start stage, i.e. initializing the classification of a large set of data with no attached…
H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
Niccoló Marini, Manfredo Atzori, Sebastian Otálora +2
Computational pathology is a domain that aims to develop algorithms to automatically analyze large digitized histopathology images, called whole slide images (WSI). WSIs are produc…
Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features
Nicola Messina, Giuseppe Amato, Fabrizio Falchi +2
Cross-modal retrieval is an important functionality in modern search engines, as it increases the user experience by allowing queries and retrieved objects to pertain to different…
Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
Nicola Messina, Giuseppe Amato, Andrea Esuli +3
Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-m…
Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs
Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet
Co-occurence networks can be adequately modeled by hyper-bag-graphs (hb-graphs for short). A hb-graph is a family of multisets having same universe, called the vertex set. An effic…
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet
Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need…
Learning by stochastic serializations
Pablo Strasser, Stephane Armand, Stephane Marchand-Maillet +1
Complex structures are typical in machine learning. Tailoring learning algorithms for every structure requires an effort that may be saved by defining a generic learning procedure…
Extracting localized information from a Twitter corpus for flood prevention
Etienne Brangbour, Pierrick Bruneau, Stéphane Marchand-Maillet +4
In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives. From the spatial persp…
Exchange-Based Diffusion in Hb-Graphs: Highlighting Complex Relationships
Xavier Ouvrard, Jean-Marie Le Goff, Stephane Marchand-Maillet
Most networks tend to show complex and multiple relationships between entities. Networks are usually modeled by graphs or hypergraphs; nonetheless a given entity can occur many tim…
Hypergraph Modeling and Visualisation of Complex Co-occurence Networks
Xavier Ouvrard, Jean-Marie Le Goff, Stephane Marchand-Maillet
Finding inherent or processed links within a dataset allows to discover potential knowledge. The main contribution of this article is to define a global framework that enables opti…
On Adjacency and e-Adjacency in General Hypergraphs: Towards a New e-Adjacency Tensor
Xavier Ouvrard, Jean-Marie Le Goff, Stephane Marchand-Maillet
In graphs, the concept of adjacency is clearly defined: it is a pairwise relationship between vertices. Adjacency in hypergraphs has to integrate hyperedge multi-adicity: the conce…
Structured nonlinear variable selection
Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs. We focus on general nonlinear functions…
Adjacency and Tensor Representation in General Hypergraphs.Part 2: Multisets, Hb-graphs and Related e-adjacency Tensors
Xavier Ouvrard, Jean-Marie Le Goff, Stephane Marchand-Maillet
HyperBagGraphs (hb-graphs as short) extend hypergraphs by allowing the hyperedges to be multisets. Multisets are composed of elements that have a multiplicity. When this multiplici…
Large-scale Nonlinear Variable Selection via Kernel Random Features
Magda Gregorová, Jason Ramapuram, Alexandros Kalousis +1
We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, no…
Adjacency and Tensor Representation in General Hypergraphs Part 1: e-adjacency Tensor Uniformisation Using Homogeneous Polynomials
Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet
Adjacency between two vertices in graphs or hypergraphs is a pairwise relationship. It is redefined in this article as 2-adjacency. In general hypergraphs, hyperedges hold for -…
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks
Magda Gregorova, Alexandros Kalousis, Stephane Marchand-Maillet
We present a new method for forecasting systems of multiple interrelated time series. The method learns the forecast models together with discovering leading indicators from within…
Networks of Collaborations: Hypergraph Modeling and Visualisation
Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet
The acknowledged model for networks of collaborations is the hypergraph model. Nonetheless when it comes to be visualized hypergraphs are transformed into simple graphs. Very often…
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build…
On Hölder projective divergences
Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet
We describe a framework to build distances by measuring the tightness of inequalities, and introduce the notion of proper statistical divergences and improper pseudo-divergences. W…
Learning Leading Indicators for Time Series Predictions
Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the syste…
Two-Stage Metric Learning
Jun Wang, Ke Sun, Fei Sha +2
In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of f…