Recent Submissions

  • Measuring the mixing of contextual information in the transformer 

    Ferrando Monsonís, Javier; Gallego Olsina, Gerard Ion; Ruiz Costa-Jussà, Marta (Association for Computational Linguistics, 2022)
    Conference lecture
    Open Access
    The Transformer architecture aggregates input information through the self-attention mechanism, but there is no clear understanding of how this information is mixed across the entire model. Additionally, recent works have ...
  • Explaining how transformers use context to build predictions 

    Ferrando Monsonís, Javier; Gallego Olsina, Gerard Ion; Tsiamas, Ioannis; Ruiz Costa-jussà, Marta (Association for Computational Linguistics, 2023)
    Conference lecture
    Open Access
    Language Generation Models produce words based on the previous context. Although existing methods offer input attributions as explanations for a model’s prediction, it is still unclear how prior words affect the model’s ...
  • Ensemble model-based method for time series sensors’ data validation and imputation applied to a real waste water treatment plant 

    Pascual Pañach, Josep; Sànchez-Marrè, Miquel; Cugueró Escofet, Miquel Àngel (International Environmental Modelling and Software Society (iEMSS), 2022)
    Conference lecture
    Open Access
    Intelligent Decision Support Systems (IDSSs) integrate different Artificial Intelligence (AI) techniques with the aim of taking or supporting human-like decisions. To this end, these techniques are based on the available ...
  • Recognition of conformational states of a G protein coupled receptor from molecular dynamic simulations using sampling techniques 

    Gutiérrez Mondragón, Mario Alberto; König, Caroline; Vellido Alcacena, Alfredo (Springer, 2023)
    Conference report
    Restricted access - publisher's policy
    Protein structures are complex and dynamic entities relevant to many biological processes. G-protein-coupled receptors in particular are a functionally relevant family of cell membrane proteins of interest as targets in ...
  • Translate first reorder later: leveraging monotonicity in semantic parsing 

    Cazzaro, Francesco; Locatelli, Davide; Quattoni, Ariadna Julieta; Carreras Pérez, Xavier (Association for Computational Linguistics, 2023)
    Conference report
    Open Access
    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 ...
  • A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma 

    Gureghian, Vincent; Herbst, Hailee; Kozar, Ines; Mihajlovic, Katarina; Malod-Dognin, Noël; Ceddia, Gaia; Angeli, Cristian; Margue, Christiane; Randic, Tijana; Philippidou, Demetra (2023-07-07)
    Article
    Open Access
    Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. ...
  • Explaining how transformers use context to build predictions 

    Ferrando Monsonís, Javier; Gallego Olsina, Gerard Ion; Tsiamas, Ioannis; Ruiz Costa-jussà, Marta (2023-05-21)
    Research report
    Open Access
    Language Generation Models produce words based on the previous context. Although existing methods offer input attributions as explanations for a model's prediction, it is still unclear how prior words affect the model's ...
  • AI-based glioma grading for a trustworthy diagnosis: an analytical pipeline for improved reliability 

    Pitarch i Abaigar, Carla; Ribas Ripoll, Vicente Jorge; Vellido Alcacena, Alfredo (2023-06-27)
    Article
    Open Access
    Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The ...
  • Adding preferences and moral values in an agent-based simulation framework for high-performance computing 

    Marin Gutierrez, David; Vázquez Salceda, Javier; Álvarez Napagao, Sergio; Gnatyshak, Dmitry (2023)
    Conference report
    Open Access
    Agent-Based Simulation is a suitable approach used now-a-days to simulate and analyze complex societal environments and scenarios. Current Agent-Based Simulation frameworks either scale quite well in computation but implement ...
  • Explainable agents adapt to human behaviour 

    Tormos Llorente, Adrián; Giménez Ábalos, Víctor; Domènech Vila, Marc; Gnatyshak, Dmitry; Álvarez Napagao, Sergio; Vázquez Salceda, Javier (2023)
    Conference report
    Open Access
    When integrating artificial agents into physical or digital environments that are shared with humans, agents are often equipped with opaque Machine Learning methods to enable adapting their behaviour to dynamic human needs ...
  • Analyzing multiple conflicts in SAT: an experimental evaluation 

    Oliveras Llunell, Albert; Rodríguez Carbonell, Enric; Zhao, Rui (EasyChair Publications, 2023)
    Conference report
    Open Access
    Unit propagation and conflict analysis are two essential ingredients of CDCL SAT Solving. The order in which unit propagation is computed does not matter when no conflict is found, because it is well known that there exists ...
  • A Docker-based federated learning framework design and deployment for multi-modal data stream classification 

    Arijit, Nandi; Xhafa Xhafa, Fatos; Kumar, Rohit (2023-05-11)
    Article
    Restricted access - publisher's policy
    In the high-performance computing (HPC) domain, federated learning has gained immense popularity. Especially in emotional and physical health analytics and experimental facilities. Federated learning is one of the most ...

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