Menu Skip to content Machine Learning Tobias Hill November 28, 2018 by Tobias Hill Part 1 – A neural network from scratch – Foundation In this series of articles I will explain the inner workings of a neural network. I will lay the foundation for the theory behind it as well as show how a competent neural network can be written in few and easy to understand lines of Java code. This is the first part in a series of articles: Part 1 – Foundation . (This article) Part 2 – Gradient descent and

首先简要介绍Hessian-Free优化理论。这是块硬骨头，并不要求一定掌握。 在给定方向上的移动能够将误差降低多少 在训练神经网络的时候，我们想要在error surface上尽量多地下降。梯度有了之后，具体能够迈多大一步呢？以二次曲线为例，给定曲线，假设其曲率为常数，并且假设梯度随error下降而减小。误差的最大减小量取决于梯度与曲率的比值，不同的方向梯

Explains how feed-forward networks provide nonlinearity in transformers, with 2-layer architecture, 4x dimension expansion, parameter analysis

Category theory can be applied to mathematically model the semantics of cognitive neural systems. We discuss semantics as a hierarchy of concepts, or symbolic descriptions of items sensed and represented in the connection weights distributed throughout a neural network. The hierarchy expresses subconcept relationships, and in a neural network it becomes represented incrementally through a Hebbian-like learning process. The categorical semantic model described here explains the learning process as the deriva

Unlocking the Secrets of Neural Similarity: A Complex Quest The quest to understand the intricacies of neural systems has led us to a fascinating crossroads. As we delve into the realm of comparing brains and AI models, a myriad of questions emerge, each more intriguing than the last. How do we deci

Modern user profiling approaches capture different forms of interactions with the data, from user-item to user-user relationships. Graph Neural Networks (GNNs) have become a natural way to model these behaviors and build efficient and effective user profiles. However, each GNN-based user profiling approach has its own way of processing information, thus creating heterogeneity that does

Inventors list Assignees list Classification tree browser Top 100 Inventors Top 100 Assignees Patent application title: RECONFIGURABLE AND CUSTOMIZABLE GENERAL-PURPOSE CIRCUITS FOR NEURAL NETWORKS Inventors: Bernard V. Brezzo (Somers, NY, US) Bernard V. Brezzo (Somers, NY, US) Leland Chang (New York, NY, US) Steven K. Esser (San Jose, CA, US) Steven K. Esser (San Jose, CA, US) Daniel J. Friedman (Sleepy Hollow, NY, US) Yong Liu (Rye, NY, US) Yong Liu (Rye, NY, US) Dharmendra S. Modha (San Jose, CA, US) Dhar

Learn to build and train a neural network from scratch. (Covering the Sequential model, Functional API, Base Model 0, and more - all with code examples

Two researchers at Shanghai University of Electric Power have recently developed and evaluated new neural network models for facial expression recognition (FER) in the wild. Their study, published in Elsevier's Neurocomputing

Emotional intelligence runs on neural circuits (interoception, right-hemisphere processing, vagal tone), not scripts. Why script-only training plateaus