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  • A joint model for 2D and 3D pose estimation from a single image 

    Simó Serra, Edgar; Quattoni, Ariadna Julieta; Torras, Carme; Moreno-Noguer, Francesc (Institute of Electrical and Electronics Engineers (IEEE), 2013)
    Text en actes de congrés
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    We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected ...
  • A latent variable ranking model for content-based retrieval 

    Quattoni, Ariadna Julieta; Carreras Pérez, Xavier; Torralba, Antonio (Springer, 2012)
    Text en actes de congrés
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    Since their introduction, ranking SVM models have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples ...
  • Spectral learning in non-deterministic dependency parsing 

    Luque, Franco M.; Quattoni, Ariadna Julieta; Balle Pigem, Borja de; Carreras Pérez, Xavier (2012)
    Text en actes de congrés
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    In this paper we study spectral learning methods for non-deterministic split head-automata grammars, a powerful hidden-state formalism for dependency parsing. We present a learning algorithm that, like other spectral ...
  • Spectral learning of sequence taggers over continuous sequences 

    Recasens, Adria; Quattoni, Ariadna Julieta (Springer-Verlag, 2013)
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    In this paper we present a spectral algorithm for learning weighted finite-state sequence taggers (WFSTs) over paired input-output sequences, where the input is continuous and the output discrete. WFSTs are an important ...
  • Spectral learning of transducers over continuous sequences 

    Recasens, Adria; Quattoni, Ariadna Julieta (2013-01-01)
    Report de recerca
    Accés obert
    In this paper we present a spectral algorithm for learning weighted nite state transducers (WFSTs) over paired input-output sequences, where the input is continuous and the output discrete. WFSTs are an important tool for ...
  • Spectral learning of weighted automata: a forward-backward perspective 

    Balle Pigem, Borja de; Carreras Pérez, Xavier; Luque, Franco M.; Quattoni, Ariadna Julieta (2013-10-07)
    Article
    Accés restringit per política de l'editorial
    In recent years we have seen the development of efficient provably correct algorithms for learning Weighted Finite Automata (WFA). Most of these algorithms avoid the known hardness results by defining parameters beyond the ...
  • Structured prediction with output embeddings for semantic image annotation 

    Quattoni, Ariadna Julieta; Ramisa Ayats, Arnau; Madhyastha, Pranava S.; Simó Serra, Edgar; Moreno-Noguer, Francesc (2016)
    Text en actes de congrés
    Accés obert
    We address the task of annotating images with semantic tuples. Solving this problem requires an algorithm able to deal with hundreds of classes for each argument of the tuple. In such contexts, data sparsity becomes a key ...
  • Unsupervised spectral learning of finite-state transducers 

    Bailly, Raphaël; Carreras Pérez, Xavier; Quattoni, Ariadna Julieta (2012)
    Text en actes de congrés
    Accés obert
    Finite-State Transducers (FST) are a standard tool for modeling paired inputoutput sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. ...
  • Unsupervised spectral learning of FSTs 

    Bailly, Raphaël; Carreras Pérez, Xavier; Quattoni, Ariadna Julieta (2013)
    Text en actes de congrés
    Accés obert
    Finite-State Transducers (FST) are a standard tool for modeling paired input output sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. ...
  • Unsupervised spectral learning of WCFG as low-rank matrix completion 

    Bailly, Raphaël; Carreras Pérez, Xavier; Luque, Franco M.; Quattoni, Ariadna Julieta (Association for Computational Linguistics, 2013)
    Comunicació de congrés
    Accés obert
    We derive a spectral method for unsupervised learning ofWeighted Context Free Grammars. We frame WCFG induction as finding a Hankel matrix that has low rank and is linearly constrained to represent a function computed by ...