Francesco Craighero

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Postdoctoral Researcher @ EPFL

Publications

  • 2024
    1. Beyond Fine-Tuning: LoRA Modules Boost Near-OOD Detection and LLM Security
      Etienne Salimbeni,  Francesco Craighero,  Renata Khasanova,  Milos Vasic,  Pierre Vandergheynst
      In ICLR 2024 Workshop on Secure and Trustworthy Large Language Models
      2024
      URL
    2. Beyond Fine-Tuning: LoRA Modules Boost Near-OOD Detection and LLM Security
      Etienne Salimbeni,  Francesco Craighero,  Renata Khasanova,  Milos Vasic,  Pierre Vandergheynst
      In 7th Deep Learning Security and Privacy Workshop (DLSP 2024)
      2024
      Best Extended Abstract Award
      URL
    3. Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations
      Mohan Vamsi Nallapareddy,  Francesco Craighero,  Cédric Gobet,  Felix Naef,  Pierre Vandergheynst
      Preprint
      2024
      URL
  • 2023
    1. LACE 2.0: An Interactive R Tool for the Inference and Visualization of Longitudinal Cancer Evolution
      Gianluca Ascolani,  Fabrizio Angaroni,  Davide Maspero,  Francesco Craighero,  Narra Lakshmi Sai Bhavesh,  Rocco Piazza,  Chiara Damiani,  Daniele Ramazzotti,  Marco Antoniotti,  Alex Graudenzi
      BMC Bioinformatics
      2023
      DOI
    2. Three Perspectives on Anomaly Detection in Deep Learning
      Francesco Craighero
      2023
      URL
    3. Unity is strength: Improving the detection of adversarial examples with ensemble approaches
      Francesco Craighero,  Fabrizio Angaroni,  Fabio Stella,  Chiara Damiani,  Marco Antoniotti,  Alex Graudenzi
      Neurocomputing
      2023
      DOI
  • 2021
    1. On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples
      Jeaneth Machicao,  Francesco Craighero,  Davide Maspero,  Fabrizio Angaroni,  Chiara Damiani,  Alex Graudenzi,  Marco Antoniotti,  Odemir M Bruno
      CURRENT GENOMICS
      2021
      DOI
    2. Combining Multi-Target Regression Deep Neural Networks and Kinetic Modeling to Predict Relative Fluxes in Reaction Systems
      Lucrezia Patruno,  Francesco Craighero,  Davide Maspero,  Alex Graudenzi,  Chiara Damiani
      Information and Computation
      2021
      DOI
  • 2020
    1. A review of computational strategies for denoising and imputation of single-cell transcriptomic data
      Lucrezia Patruno,  Davide Maspero,  Francesco Craighero,  Fabrizio Angaroni,  Marco Antoniotti,  Alex Graudenzi
      Briefings in Bioinformatics
      2020
      DOI
    2. Understanding Deep Learning with Activation Pattern Diagrams
      Francesco Craighero,  Alex Graudenzi,  Fabrizio Angaroni,  Fabio Stella,  Marco Antoniotti
      In Proceedings of the Italian Workshop on Explainable Artificial Intelligence co-located with 19th International Conference of the Italian Association for Artificial Intelligence, XAI.it@AIxIA 2020, Online Event, November 25-26, 2020
      2020
      URL
    3. Investigating the Compositional Structure of Deep Neural Networks
      Francesco Craighero,  Fabrizio Angaroni,  Alex Graudenzi,  Fabio Stella,  Marco Antoniotti
      In Machine Learning, Optimization, and Data Science
      2020
      DOI
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