Machine learning techniques are designed to mathematically emulate the functions and structure of neurons and neural networks in the brain. However, biological neurons are very complex, which makes artificially replicating
1. どんなもの？
Garcez et al.'s paper merges neural networks with symbolic reasoning to build interpretable, accountable AI systems using scalable, principled methods
Chainer latest Tutorials Examples References Other Community Chainer Docs » Chainer – A flexible framework of neural networks Edit on GitHub Chainer – A flexible framework of neural networks ¶ Chainer is a powerful, flexible and intuitive deep learning framework. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets
Researchers from MIT identified a new "Pizza algorithm" for modular arithmetic, distinct from the previously known "Clock algorithm," demonstrating that neural networks can learn diverse internal
Abstract page for arXiv paper 2404.07168v1: Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots
This work addresses the challenges of bringing Machine Learning to microcontroller units (MCUs), where we focus on the ubiquitous ARM Cortex-M architecture.
Forecasting of market prices is a basis of rational decision making [Zim94]. Especially recurrent neural networks (RNN) offer a framework for the computation of a complete temporal development. Our applications include short- (20 days) and long-term (52 weeks
Developers tend to handle problems with conditional statements and loops. This is the number one topic of developers and data scientists being separated from each other. Herein, vectorization enables to create clean code and increase code performance.
# Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch Aojun Zhou ⋅ Yukun Ma ⋅ Junnan Zhu ⋅ Jianbo Liu ⋅ Zhijie Zhang ⋅ Kun Yuan ⋅ Wenxiu Sun ⋅ Hongsheng Li Keywords: sparsity efficient training and inference. Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into unstructured fine-grained sparsity that zeroes out multiple individual weights distributed