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This paper investigates an isolated setting of the lexical substitution task of replacing words with their synonyms. In particular, we examine this problem in the setting of subtitle generation and evaluate state of the art scoring methods that predict the ...
The principal objective of this thesis is to investigate approaches toward a robust automatic face authentication (AFA) system in weakly constrained environments. In this context, we develop new algorithms based on local features and generative models. In ...
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prior distribution on a class of simulators (generative mode ...
Minimizing communication cost is a fundamental problem in large-scale federated sensor networks. Maintaining model-based views of data streams has been highlighted because it permits efficient data communication by transmitting parameter values of models, ...
This thesis proposes to analyse symbolic musical data under a statistical viewpoint, using state-of-the-art machine learning techniques. Our main argument is to show that it is possible to design generative models that are able to predict and to generate m ...
In this thesis, we address the problem of face modelling by using dedicated statistical generative models, with an application to the face authentication task. Face authentication consists in either accepting or rejecting a user's claim supported by its fa ...
In this paper we propose a new approach to automatic human face detection which employs anisotropic Gaussian filters as local image descriptors. We then show how the paradigm of classifier combination can be used for building a face detector that outperfor ...
This thesis proposes to analyse symbolic musical data under a statistical viewpoint, using state-of-the-art machine learning techniques. Our main argument is to show that it is possible to design generative models that are able to predict and to generate m ...
This paper investigates an isolated setting of the lexical substitution task of replacing words with their synonyms. In particular, we examine this problem in the setting of subtitle generation and evaluate state of the art scoring methods that predict the ...
Limited data and low dose constraints are common problems in a variety of tomographic reconstruction paradigms which lead to noisy and incomplete data. Over the past few years sinogram denoising has become an essential pre-processing step for low dose Comp ...
