297 citations · 598 across the 12 of their papers we have counts for
14 papers
Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems
Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone +2
Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative s…
Conditioned Query Generation for Task-Oriented Dialogue Systems
Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone +2
Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative s…
Spoken Language Understanding on the Edge
Alaa Saade, Alice Coucke, Alexandre Caulier +9
We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contributions are twofold. First, we outline the design…
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
Alice Coucke, Alaa Saade, Adrien Ball +9
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT de…
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
Eric W. Tramel, Marylou Gabrié, Andre Manoel +2
Restricted Boltzmann machines (RBMs) are energy-based neural-networks which are commonly used as the building blocks for deep architectures neural architectures. In this work, we d…
Recovering asymmetric communities in the stochastic block model
Francesco Caltagirone, Marc Lelarge, Léo Miolane
We consider the sparse stochastic block model in the case where the degrees are uninformative. The case where the two communities have approximately the same size has been extensiv…
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
Eric W. Tramel, Andre Manoel, Francesco Caltagirone +2
In this work, we consider compressed sensing reconstruction from measurements of -sparse structured signals which do not possess a writable correlation model. Assuming that…
Random Projections through multiple optical scattering: Approximating kernels at the speed of light
Alaa Saade, Francesco Caltagirone, Igor Carron +4
Random projections have proven extremely useful in many signal processing and machine learning applications. However, they often require either to store a very large random matrix,…
Spectral Detection on Sparse Hypergraphs
Maria Chiara Angelini, Francesco Caltagirone, Florent Krzakala +1
We consider the problem of the assignment of nodes into communities from a set of hyperedges, where every hyperedge is a noisy observation of the community assignment of the adjace…
Blind Sensor Calibration using Approximate Message Passing
Christophe Schülke, Francesco Caltagirone, Lenka Zdeborová
The ubiquity of approximately sparse data has led a variety of com- munities to great interest in compressed sensing algorithms. Although these are very successful and well underst…
On Convergence of Approximate Message Passing
Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová
Approximate message passing is an iterative algorithm for compressed sensing and related applications. A solid theory about the performance and convergence of the algorithm exists…
Properties of spatial coupling in compressed sensing
Francesco Caltagirone, Lenka Zdeborová
In this paper we address a series of open questions about the construction of spatially coupled measurement matrices in compressed sensing. For hardware implementations one is forc…
Dynamics and termination cost of spatially coupled mean-field models
Francesco Caltagirone, Silvio Franz, Richard Morris +1
This work is motivated by recent progress in information theory and signal processing where the so-called `spatially coupled' design of systems leads to considerably better perform…
Blind Calibration in Compressed Sensing using Message Passing Algorithms
Christophe Schülke, Francesco Caltagirone, Florent Krzakala +1
Compressed sensing (CS) is a concept that allows to acquire compressible signals with a small number of measurements. As such it is very attractive for hardware implementations. Th…