Ara es mostren els items 1-16 de 16

    • 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
      Accés restringit per política de l'editorial
      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
      Accés restringit per política de l'editorial
      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 ...
    • Analyzing text representations by measuring task alignment 

      González Gutiérrez, César; Primadhanty, Audi; Cazzaro, Francesco; Quattoni, Ariadna Julieta (Association for Computational Linguistics, 2023)
      Text en actes de congrés
      Accés obert
      Textual representations based on pre-trained language models are key, especially in few-shot learning scenarios. What makes a representation good for text classification? Is it due to the geometric properties of the space ...
    • Learning task-specific bilexical embeddings 

      Madhyastha, Pranava S.; Carreras Pérez, Xavier; Quattoni, Ariadna Julieta (2014)
      Text en actes de congrés
      Accés obert
      We present a method that learns bilexical operators over distributional representations of words and leverages supervised data for a linguistic relation. The learning algorithm exploits lowrank bilinear forms and induces ...
    • Measuring alignment bias in neural Seq2Seq semantic parsers 

      Locatelli, Davide; Quattoni, Ariadna Julieta (Association for Computational Linguistics, 2022)
      Text en actes de congrés
      Accés obert
      Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations. ...
    • Semantic tuples for evaluation of image sentence generation 

      Ellebracht, Lily Delores; Ramisa Ayats, Arnau; Shantharam Madhyastha, Pranava Swaroop; Cordero Rama, Jose Alejandro; Moreno-Noguer, Francesc; Quattoni, Ariadna Julieta (2015)
      Text en actes de congrés
      Accés obert
      The automatic generation of image captions has received considerable attention. The problem of evaluating caption generation systems, though, has not been that much explored. We propose a novel evaluation approach based ...
    • 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
      Accés restringit per política de l'editorial
      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)
      Text en actes de congrés
      Accés restringit per política de l'editorial
      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 ...
    • Spectral regularization for max-margin sequence tagging 

      Quattoni, Ariadna Julieta; Balle Pigem, Borja de; Carreras Pérez, Xavier; Globerson, Amir (2014)
      Text en actes de congrés
      Accés obert
      We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can ...
    • 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 ...
    • Translate first reorder later: leveraging monotonicity in semantic parsing 

      Cazzaro, Francesco; Locatelli, Davide; Quattoni, Ariadna Julieta; Carreras Pérez, Xavier (Association for Computational Linguistics, 2023)
      Text en actes de congrés
      Accés obert
      Prior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks. This limitation led to a resurgence of methods that model alignments between sentences and their ...
    • 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 ...