← A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs → # Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces 皮質内ブレインマシンインターフェース (iBMI) は、対麻痺のある人々の日常活動を行う能力を回復することで、その生活を劇的に改善する可能性を秘めています。 ただし、現在の
A human brain determines what’s inside a picture by looking for features. We have a complex set of filters in our mind which processes images as they come in, throws away most of the information, and gives us summaries to our mind, which decides what we are looking at. CNN’s are doing the same thing…
# Graph Clustering with Graph Neural Networks Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller; 24(127):1−21, 2023. ### Abstract Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. Graph clustering has the same overall goal as node pooling in GNNs—does this mean that GNN
# Recurrent Neural Network A Recurrent Neural Network (RNN) is a class of artificial neural network that has memory or feedback loops that allow it to better recognize patterns in data. RNNs are an extension of regular artificial neural networks that add connections feeding the hidden layers of the neural network back into themselves - these are called recurrent connections. The recurrent connections provide a recurrent network with visibility of not just the current data sample it has been provided, but a
Abstract page for arXiv paper 2010.09570: Bayesian Neural Networks with Soft Evidence
The Identity-aware Graph Neural Networks (ID-GNNs) framework enhances the expressive power of message-passing GNNs beyond the 1-Weisfeiler-Lehman test by inductively injecting identity information
# NeuralProphet: A Time-Series Modeling Python Library based on Neural-Networks Machine Learning Neural-Network Python Library Time Series Analysis Tutorial Author Esmaeil Alizadeh Published December 3, 2020 ## On this page - NeuralProphet Library - NeuralProphet vs. Prophet - Project Maintainers - Installation - Implementation with a Case Study Note 👉 This article is also published on Towards Data Science blog . NeuralProphet 1 is a python library for modeling time-series data based on neura
# The Problem of Reproducability in Neural Networks Published 2022-12-01 by Kevin Feasel Pete Warden explains a problem : Last week I had a question from a colleague about reproducibility in TensorFlow, specifically in the 1.14 era. He wanted to be able to run the same training code multiple times and get exactly the same results, which on the surface doesn’t seem like an unreasonable expectation. Machine learning training is fundamentally a series of arithmetic operations applied repeatedly, so what mak
Learn what is deep learning and how neural networks actually work. Simple explanations of layers, training, and why deep learning powers today's best AI
Computing the expectation of the description length for neural networks