Pre-paper

This isn’t some of my weekly posts, is just a discoberiment that I’ve done, and I wanted your opinion.

A Simple Look at Neural Architectures Inspired by Object-Oriented Programming: Towards Hybrid Neural-Probabilistic Models Litus Ramos March 2025 1 Introduction Hi, my name is Litus, and I want to suggest a way to combine object-oriented programming with neural networks. My idea is to explore if this could help create hybrid models that use both neural networks and probabilistic methods. 2 The idea behind it I started thinking about how neural networks are set up, especially how the neurons in each layer are connected, and how layers build on each other. I was learning about ”backpropagation” and how information, like error gradients, moves from one layer to another during training, creating a flow of knowledge. This got me thinking: what if we look at neural networks like object-oriented programming (OOP)? In OOP, we have classes that can inherit properties and behaviors from other classes. This makes it easy to reuse and organize code. Neural networks also have a kind of hierarchy: each layer builds on the work done by the layer before it, like a child class inheriting from a parent class. So, layers pass on more complex information while still depending on the basic work done in previous layers. This made me think that we could apply OOP ideas to improve how neural networks are set up. 3 Possible uses By using ideas from OOP in neural networks, we might design more flexible and modular networks. This means each layer could specialize in one task, but still benefit from what the previous layers have done. One potential application could be hybrid models that mix neural networks with probabilistic models. This would allow the model to not only learn from data but also deal with uncertainty, which could help in tasks where predictions need to handle noise, like reinforcement learning or probabilistic programming. This type of hybrid 1model could work better in situations where data is unclear or incomplete. Another possible application could involve making models easier to understand. In OOP, inheritance makes it clear how different classes are connected. If we apply this to neural networks, we could create models where it’s easier to see what each layer is doing. This could be helpful in areas like medicine or finance, where understanding how a model makes decisions is important. Moreover, we could apply neural-symbolic systems, which combine neural networks with symbolic reasoning. This could improve a model’s ability to solve complex problems that need both data learning and logical reasoning. Finally, making networks modular could help with transfer learning, where a model trained on one task can easily be adapted to a different one. This could make training faster and reduce the amount of data needed. 4 Challenges and Uncertainties Challenges and Uncertainties Although the idea is interesting, there are still several challenges to solve. I’m not sure how to put everything together yet. While the ideas seem clear, making them work in practice will require more research and testing. A big challenge is figuring out how to design the layers so they can ”inherit” information properly. Also, combining probabilistic models with neural networks isn’t easy. It requires a deeper understanding of both fields. Another challenge is combining symbolic reasoning with neural networks. It’s hard to link the continuous, data-driven part of neural networks with the more structured, logical reasoning used in symbolic systems. Finding a way to make both approaches work well together is a tough problem. Even though I don’t have all the answers right now, I believe this idea could lead to important discoveries. Creating more flexible and understandable models is a promising goal. With more time and research, I hope to solve these problems. 5 Conclusion In conclusion, combining object-oriented programming ideas with neural net- works is still in the early stages, but it could change how we design and improve neural architectures. This could make networks more modular, understandable, and adaptable. There are many challenges to solve, but I believe the benefits are worth it. I hope to keep exploring this idea and developing it as I learn more.

Disclaimer:

This is a personal idea still in its early stages. I share it to inspire others and contribute to the community. If anyone wants to build on this idea or use it, I would appreciate being cited as the original author, thanks

<p xmlns:cc="creativecommons.org/ns#" xmlns:dct="purl.org/dc/terms"><a property="dct:title" rel="cc:attributionURL" href="litus.hashnode.dev/pre-paper">A Simple Look at Neural Architectures Inspired by Object-Oriented Programming: Towards Hybrid Neural-Probabilistic Models</a> by <span property="cc:attributionName">Litus Ramos Puig </span> is licensed under <a href="creativecommons.org/licenses/by-sa/4.0/?ref.." target="_blank" rel="license noopener noreferrer" style="display:inline-block;">Creative Commons Attribution-ShareAlike 4.0 International<img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="mirrors.creativecommons.org/presskit/icons/.." alt=""><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="mirrors.creativecommons.org/presskit/icons/.." alt=""><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="mirrors.creativecommons.org/presskit/icons/.." alt=""></a></p>

Please, if somebody wants to continue my researching, please cite this publication, more info about me in litus.hashnode.dev/pre-paper

This work is licensed, I post it in medium, you could search it!!! There is more complete!

Signature 5th of March 2025. Litus Ramos. 13 years old. Terrassa, Barcelona, Spain