The article describes a small NumPy reproduction of subliminal learning, demonstrating how a neural network can learn to read digits from pure noise. It provides a concise implementation and a brief argument explaining why this phenomenon occurs.
The article "Building and Visualising Kolmogorov–Arnold Networks" explores the concept of neural networks that learn functions rather than just weights. It discusses the construction and visualization of Kolmogorov–Arnold networks, providing insights into how these networks operate and how they can be represented visually. The post was originally published on Towards Data Science.
Understanding how FPN allows deep learning models detecting small objects and how to implement it from scratch The post FPN Paper Walkthrough: Leveraging the Internal Pyramid appeared first on Towards Data Science .
Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .
The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science .
arXiv:2511. 07308v3 Announce Type: replace Abstract: Understanding the training dynamics of deep neural networks remains a major open problem, with physics-inspired approaches offering promising insights.
arXiv:2601. 04539v2 Announce Type: replace-cross Abstract: In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning.
arXiv:2606. 13801v1 Announce Type: new Abstract: Neural responses in cortex exhibit substantial trial-to-trial variability in response to repeated stimuli, while peripheral sensory neurons respond far more consistently, leading many to wonder whether stochasticity may carry meaning.
arXiv:2606. 04583v1 Announce Type: new Abstract: Many researchers investigated neural networks with some of their weights fixed to values randomly drawn from a given distribution, e.
arXiv:2607. 23397v1 Announce Type: new Abstract: Hierarchical neural networks are widely used in artificial intelligence, yet their mathematical properties remain incompletely understood.
arXiv:2402. 00152v5 Announce Type: replace Abstract: Constructing the architecture of a neural network is a challenging pursuit for the machine learning community, and the dilemma of whether to go deeper or wider remains a persistent question.
arXiv:2607. 19042v1 Announce Type: cross Abstract: Neural hypergraphs are a natural generalization of neural networks, the reference models in modern machine learning.
The article describes how the author replicated Anthropic’s “Toy Models of Superposition” using only NumPy, hand‑derived gradients, and no external libraries. By training a very small neural network to compress data, the model produced a pentagon shape as its output. The post showcases a minimal, from‑scratch implementation of a complex concept in machine learning.