Now showing items 1-10 of 10

    • 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)
      Conference report
      Restricted access - publisher's policy
      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)
      Conference report
      Restricted access - publisher's policy
      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)
      Conference report
      Restricted access - publisher's policy
      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)
      Conference report
      Restricted access - publisher's policy
      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)
      External research report
      Open Access
      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
      Restricted access - publisher's policy
      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)
      Conference report
      Open Access
      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)
      Conference report
      Open Access
      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)
      Conference report
      Open Access
      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)
      Conference lecture
      Open Access
      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 ...