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<titleInfo><title>Machine unlearning using forgetting neural networks</title></titleInfo>


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<name type="personal">
  <namePart type="given">Amartya</namePart>
  <namePart type="family">Hatua</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Trung T.</namePart>
  <namePart type="family">Nguyen</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
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  <namePart type="given">Filip</namePart>
  <namePart type="family">Cano Cordoba</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">708cad98-e86a-11ef-8098-bdae2d7c6af1</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-0783-904X</description></name>
<name type="personal">
  <namePart type="given">Andrew H.</namePart>
  <namePart type="family">Sung</namePart>
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  <namePart>ICAART: International Conference on Agents and Artificial Intelligence</namePart>
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  <namePart>Vigilant Algorithmic Monitoring of Software</namePart>
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<abstract lang="eng"> 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.</abstract>

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    <url displayLabel="2026_CAART_Hatua.pdf">https://research-explorer.ista.ac.at/download/22104/23178/2026_CAART_Hatua.pdf</url>
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<originInfo><publisher>Science and Technology Publications</publisher><dateIssued encoding="w3cdtf">2026</dateIssued><place><placeTerm type="text">Marbella, Spain</placeTerm></place>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<subject><topic>Machine Unlearning</topic><topic>Neuroscience-Inspired Machine Learning</topic><topic>Membership Inference Attacks</topic>
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<relatedItem type="host"><titleInfo><title>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</title></titleInfo>
  <identifier type="issn">2184-3589</identifier>
  <identifier type="eIssn">2184-433X</identifier>
  <identifier type="isbn">9789897587962</identifier>
  <identifier type="arXiv">2410.22374</identifier><identifier type="doi">10.5220/0014326500004052</identifier>
<part><detail type="volume"><number>2</number></detail><extent unit="pages">1536-1546</extent>
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<ama>Hatua A, Nguyen TT, Cano Cordoba F, Sung AH. Machine unlearning using forgetting neural networks. In: &lt;i&gt;Proceedings of the 18th International Conference on Agents and Artificial Intelligence&lt;/i&gt;. Vol 2. Science and Technology Publications; 2026:1536-1546. doi:&lt;a href=&quot;https://doi.org/10.5220/0014326500004052&quot;&gt;10.5220/0014326500004052&lt;/a&gt;</ama>
<mla>Hatua, Amartya, et al. “Machine Unlearning Using Forgetting Neural Networks.” &lt;i&gt;Proceedings of the 18th International Conference on Agents and Artificial Intelligence&lt;/i&gt;, vol. 2, Science and Technology Publications, 2026, pp. 1536–46, doi:&lt;a href=&quot;https://doi.org/10.5220/0014326500004052&quot;&gt;10.5220/0014326500004052&lt;/a&gt;.</mla>
<ista>Hatua A, Nguyen TT, Cano Cordoba F, Sung AH. 2026. Machine unlearning using forgetting neural networks. Proceedings of the 18th International Conference on Agents and Artificial Intelligence. ICAART: International Conference on Agents and Artificial Intelligence vol. 2, 1536–1546.</ista>
<ieee>A. Hatua, T. T. Nguyen, F. Cano Cordoba, and A. H. Sung, “Machine unlearning using forgetting neural networks,” in &lt;i&gt;Proceedings of the 18th International Conference on Agents and Artificial Intelligence&lt;/i&gt;, Marbella, Spain, 2026, vol. 2, pp. 1536–1546.</ieee>
<short>A. Hatua, T.T. Nguyen, F. Cano Cordoba, A.H. Sung, in:, Proceedings of the 18th International Conference on Agents and Artificial Intelligence, Science and Technology Publications, 2026, pp. 1536–1546.</short>
<chicago>Hatua, Amartya, Trung T. Nguyen, Filip Cano Cordoba, and Andrew H. Sung. “Machine Unlearning Using Forgetting Neural Networks.” In &lt;i&gt;Proceedings of the 18th International Conference on Agents and Artificial Intelligence&lt;/i&gt;, 2:1536–46. Science and Technology Publications, 2026. &lt;a href=&quot;https://doi.org/10.5220/0014326500004052&quot;&gt;https://doi.org/10.5220/0014326500004052&lt;/a&gt;.</chicago>
<apa>Hatua, A., Nguyen, T. T., Cano Cordoba, F., &amp;#38; Sung, A. H. (2026). Machine unlearning using forgetting neural networks. In &lt;i&gt;Proceedings of the 18th International Conference on Agents and Artificial Intelligence&lt;/i&gt; (Vol. 2, pp. 1536–1546). Marbella, Spain: Science and Technology Publications. &lt;a href=&quot;https://doi.org/10.5220/0014326500004052&quot;&gt;https://doi.org/10.5220/0014326500004052&lt;/a&gt;</apa>
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