---
res:
  bibo_abstract:
  - Modern computer systems store vast amounts of personal data, enabling advances
    in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes
    desired for an ML model to forget part of the data it was trained on. In this
    paper, we introduce a novel unlearning approach based on Forgetting Neural Networks
    (FNNs), a neuroscience-inspired architecture that explicitly encodes forgetting
    through multiplicative decay factors. While FNNs had previously been studied as
    a theoretical construct, we provide the first concrete implementation and demonstrate
    their effectiveness for targeted unlearning. We propose several variants with
    per-neuron forgetting factors, including rank-based assignments guided by activation
    levels, and evaluate them on MNIST and Fashion-MNIST benchmarks. Our method systematically
    removes information associated with forget sets while preserving performance on
    retained data. Membership inference attacks confirm the effectiveness of FNN-based
    unlearning in erasing information about the training data from the neural network.
    These results establish FNNs as a promising foundation for efficient and interpretable
    unlearning. @eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Amartya
      foaf_name: Hatua, Amartya
      foaf_surname: Hatua
  - foaf_Person:
      foaf_givenName: Trung
      foaf_name: Nguyen, Trung
      foaf_surname: Nguyen
  - foaf_Person:
      foaf_givenName: Filip
      foaf_name: Cano Cordoba, Filip
      foaf_surname: Cano Cordoba
      foaf_workInfoHomepage: http://www.librecat.org/personId=708cad98-e86a-11ef-8098-bdae2d7c6af1
    orcid: 0000-0002-0783-904X
  - foaf_Person:
      foaf_givenName: Andrew
      foaf_name: Sung, Andrew
      foaf_surname: Sung
  bibo_doi: 10.5220/0014326500004052
  bibo_volume: 2
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2184-433X
  - http://id.crossref.org/issn/9789897587962
  dct_language: eng
  dct_publisher: SciTePress@
  dct_subject:
  - Machine Unlearning
  - Neuroscience-Inspired Machine Learning
  - Membership Inference Attacks
  dct_title: Machine unlearning using forgetting neural networks@
...