This paper introduces adaptive mutual information estimation techniques to quantify compression in deep neural networks and reveal its role in generalization
Supervised Learning in Multilayer Neural Networks Neural networks consist of simple processing units that interact via weighted connections. They are sometimes implemented in hardware but most research involves software simulations. They were originally inspired by ideas about how the brain computes, and understanding biological computation is still the major goal of many researchers in the field (Churchland and Sejnowski 1992). However, some biologically unrealistic neural networks are both computationally
日本語 English Center for Information and Neural Networks 日本語 Center for Information and Neural Networks 〒565-0871 Osaka Prefecture Suita City Yamadaoka 1-4 Center for Information and Neural Networks (CiNet) 2nd floor Member Research Manager Masahiko Haruno Professor of engineering. I am interested in the neuroinformatics of decision-making, especially making decisions and learning in social situations. Recently, it is decreasing, but during the free time, I travel around the world to see various
Deep Learning Course Lecture 01: Introduction to Neural Networks Lecture 02: Training Neural Networks A Simple Neural Network Example Section 1: A Simple Neural Network Example Example: A Neural Network for the XOR Function Example: Define Our Neural Network Example: Solving our Network Example: Solving our Network (continued) Example: Illustration of Non-Linear Hidden Layers Optimization and Gradient Descent Section 2: Optimization and Gradient Descent Section 2 Questions Optimization Review Most Optimizat
A new arXiv paper presents the first systematic benchmark of Sheaf Neural Networks (SNNs) under inductive protocols, evaluating three diffusion mechanisms across the sheaf design space. The work addresses the gap left by prior SNN evaluations, which focused almost exclusively on transductive node cl
# Artificial Neural Network An artificial neural network is a biologically inspired computational model that is patterned after the network of neurons present in the human brain. Artificial neural networks can also be thought of as learning algorithms that model the input-output relationship. Applications of artificial neural networks include pattern recognition and forecasting in fields such as medicine, business, pure sciences, data mining, telecommunications, and operations managements. An artificial n
A research team lead by Geoffrey Hinton has created an imaginary vision system called GLOM that enables neural networks with fixed architecture to parse an image into a part-whole hierarchy with different structures for each image
When designing neural networks to handle language, one of the central design decisions you have to make is how you model the flow of time. The words in a sentence do not have a random order, but the order in which they appear is central to their meaning and correct grammar. Ignoring this order completely
Skip to content Interpretable neural networks, human and artificial In Spring of 2025, researchers at the AI company Anthropic released a pair of papers in which they document a method for making the internal workings of a large language model like Claude interpretable rather than a mysterious black box. They also provide many case studies where they are able to probe the inner workings of Claude during tasks like multi-step reasoning, planning while composing poetry, pursuing secret goals, generalizing fro
In this article, learn AI and machine learning basics, from neural networks to NLP, embeddings, and text analysis, in a clear, beginner-friendly guide