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When new data is encountered, the models\nmust inefficiently relearn their parameters to adequately incorporate the new\ninformation without catastrophic interference. Architectures with augmented\nmemory capacities, such as Neural Turing Machines (NTMs), offer the ability to\nquickly encode and retrieve new information, and hence can potentially obviate\nthe downsides of conventional models. Here, we demonstrate the ability of a\nmemory-augmented neural network to rapidly assimilate new data, and leverage\nthis data to make accurate predictions after only a few samples. We also\nintroduce a new method for accessing an external memory that focuses on memory\ncontent, unlike previous methods that additionally use memory location-based\nfocusing mechanisms.","url_abs":"http://arxiv.org/abs/1605.06065v1","url_pdf":"http://arxiv.org/pdf/1605.06065v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/Gaopeng-Bai/MANN_model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/YoungHector/one-shot-mann","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/Zelgunn/MANN_tf_keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/adityagilra/archibrain","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/ash3n/One-shot-Memory-Augmented-NN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/hmishra2250/NTM-One-Shot-TF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/philippe554/MANN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/snowkylin/ntm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"LGPL-3.0"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/tristandeleu/ntm-one-shot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/vineetjain96/one-shot-mann","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"one-shot-learning-with-memory-augmented","repo_url":"https://github.com/vivekatwal/projects","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"one-shot-learning","task_name":"One-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.06065","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.06065"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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