Now showing items 21-22 of 22

    • Explaining Black Box with Visual Exploration of Latent Space 

      Bodria, Francesco; Rinzivillo, Salvatore; Fadda, Daniele; Guidotti, Riccardo; Giannotti, Fosca; Pedreschi, Dino (The Eurographics Association, 2022)
      Autoencoders are a powerful yet opaque feature reduction technique, on top of which we propose a novel way for the joint visual exploration of both latent and real space. By interactively exploiting the mapping between ...
    • Visual Evaluation of Translation Alignment Data 

      Yousef, Tariq; Jänicke, Stefan (The Eurographics Association, 2022)
      Translation alignment plays a crucial role in various applications in natural language processing and digital humanities. With the recent advance in neural machine translation and contextualized language models, numerous ...