Wikipedia Easy Search

This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages)

This article relies excessively on references to primary sources. Please improve this article by adding secondary or tertiary sources.
Find sources: "History of artificial neural networks" – news · newspapers · books · scholar · JSTOR (August 2022) (Learn how and when to remove this message)

This article needs to be updated. Please help update this article to reflect recent events or newly available information. (September 2021)

This article contains several duplicated citations.

The reason given is: DuplicateReferences detected:

https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf (refs: 5, 148)
https://www.degruyter.com/view/books/9781400882618/9781400882618-002/9781400882618-002.xml (refs: 11, 12)
https://arxiv.org/abs/2212.11279 (refs: 26, 70)
https://arxiv.org/abs/1404.7828 (refs: 52, 96)
https://arxiv.org/abs/1411.4555 (refs: 65, 103)
https://www.cs.princeton.edu/courses/archive/spr08/cos598B/Readings/Fukushima1980.pdf (refs: 67, 76)
https://arxiv.org/abs/1409.1556 (refs: 101, 106)

It is recommended to use named references to consolidate citations that are used multiple times. (September 2024) (Learn how and when to remove this message)

(Learn how and when to remove this message)

Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Deep learning Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Boltzmann machine Restricted GAN Diffusion model SOM Convolutional neural network U-Net LeNet AlexNet DeepDream Neural radiance field Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Journals and conferences ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
v t e

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural circuitry.^[1]^[a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron.^[1] Little research was conducted on ANNs in the 1970s and 1980s, with the AAAI calling this period an "AI winter".^[2]

Later, advances in hardware and the development of the backpropagation algorithm, as well as recurrent neural networks and convolutional neural networks, renewed interest in ANNs. The 2010s saw the development of a deep neural network (i.e., one with many layers) called AlexNet.^[3] It greatly outperformed other image recognition models, and is thought to have launched the ongoing AI spring, and further increasing interest in deep learning.^[4] The transformer architecture was first described in 2017 as a method to teach ANNs grammatical dependencies in language,^[5] and is the predominant architecture used by large language models such as GPT-4. Diffusion models were first described in 2015, and became the basis of image generation models such as DALL-E in the 2020s.^{[citation needed]}

^ ^a ^b Rosenblatt, F. (1958). "The Perceptron: A Probabilistic Model For Information Storage And Organization In The Brain". Psychological Review. 65 (6): 386–408. CiteSeerX 10.1.1.588.3775. doi:10.1037/h0042519. PMID 13602029. S2CID 12781225.
^ Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3.
^ Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. (2017-05-24). "ImageNet classification with deep convolutional neural networks" (PDF). Communications of the ACM. 60 (6): 84–90. doi:10.1145/3065386. ISSN 0001-0782. S2CID 195908774.
^ Gershgorn, Dave (26 July 2017). "The data that transformed AI research—and possibly the world". Quartz.
^ Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N; Kaiser, Łukasz; Polosukhin, Illia (2017). "Attention is All you Need" (PDF). Advances in Neural Information Processing Systems. 30. Curran Associates, Inc.

Cite error: There are <ref group=lower-alpha> tags or {{efn}} templates on this page, but the references will not show without a {{reflist|group=lower-alpha}} template or {{notelist}} template (see the help page).