Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.06631 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910907074871296 |
|---|---|
| author | Inazawa, Hiroshi |
| author_facet | Inazawa, Hiroshi |
| contents | In this paper, we propose a mechanism for storing complex patterns within a neural network and subsequently recalling them. This model is based on our work published in 2018(Inazawa, 2018), which we have refined and extended in this work. With the recent advancements in deep learning and large language model (LLM)-based AI technologies (generative AI), it can be considered that methodologies for the learning are becoming increasingly well-established. In the future, we expect to see further research on memory using models based on Transformers (Vaswani, et. al., 2017, Rae, et. al., 2020), but in this paper we propose a simpler and more powerful model of memory and recall in neural networks. The advantage of storing patterns in a neural network lies in its ability to recall the original pattern even when an incomplete version is presented. The patterns we have produced for use in this study have been QR code (DENSO WAVE, 1994), which has become widely used as an information transmission tool in recent years. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Method for Storing Patterns in Neural Networks-Memorization and Recall of QR code Patterns- Inazawa, Hiroshi Neural and Evolutionary Computing In this paper, we propose a mechanism for storing complex patterns within a neural network and subsequently recalling them. This model is based on our work published in 2018(Inazawa, 2018), which we have refined and extended in this work. With the recent advancements in deep learning and large language model (LLM)-based AI technologies (generative AI), it can be considered that methodologies for the learning are becoming increasingly well-established. In the future, we expect to see further research on memory using models based on Transformers (Vaswani, et. al., 2017, Rae, et. al., 2020), but in this paper we propose a simpler and more powerful model of memory and recall in neural networks. The advantage of storing patterns in a neural network lies in its ability to recall the original pattern even when an incomplete version is presented. The patterns we have produced for use in this study have been QR code (DENSO WAVE, 1994), which has become widely used as an information transmission tool in recent years. |
| title | The Method for Storing Patterns in Neural Networks-Memorization and Recall of QR code Patterns- |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2504.06631 |