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Microsoft open source library
NNI (Neural Network Intelligence) is a free and open-source AutoML toolkit developed by Microsoft. It is used to automate feature engineering, model compression
Neural_Network_Intelligence
Structure in biology and artificial intelligence
neural network is a mathematical model used to approximate nonlinear functions. Artificial neural networks are used to solve artificial intelligence problems
Neural_network
optimization tool using genetic programming Neural Network Intelligence – Microsoft toolkit for hyperparameter tuning and neural architecture search MindsDB – AutoML
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
Structure in nervous systems
A neural network, also called a neuronal network, is an interconnected population of neurons typically containing multiple neural circuits. Biological
Neural_network_(biology)
Computational model used in machine learning
neural network (NN) or neural net, is a computational model inspired by the structure and functions of biological neural networks. A neural network consists
Neural network (machine learning)
Neural_network_(machine_learning)
Type of software
biological neural networks, and in some cases, a wider array of adaptive systems such as artificial intelligence and machine learning. Neural network simulators
Neural_network_software
Class of artificial neural networks
Graph neural networks (GNNs) are artificial neural networks designed for tasks whose inputs are graphs. Because graphs usually do not have a canonical
Graph_neural_network
Type of feedforward neural network
A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep
Convolutional_neural_network
Class of artificial neural network
In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where
Recurrent_neural_network
Branch of machine learning
machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
Artificial neural network that mimics neurons
Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes
Spiking_neural_network
Intelligence of machines
space search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics
Artificial_intelligence
Trimming artificial neural networks to reduce computational overhead
an existing artificial neural network. The goal of this process is to reduce the size (parameter count) of the neural network (and therefore the computational
Pruning (artificial neural network)
Pruning_(artificial_neural_network)
Type of neural network which utilizes recursion
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce
Recursive_neural_network
Type of artificial neural network
machine learning, a neural field (also known as implicit neural representation, neural implicit, or coordinate-based neural network), is a mathematical
Neural_field
Technique to solve partial differential equations
In machine learning, physics-informed neural networks (PINNs), also referred to as theory-trained neural networks (TTNs), are a type of universal function
Physics-informed neural networks
Physics-informed_neural_networks
Type of artificial neural network
A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions
Residual_neural_network
Artificial neural network architecture
In artificial intelligence, a differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (but not
Differentiable neural computer
Differentiable_neural_computer
Type of artificial neural network
A feedforward neural network is an artificial neural network in which information flows in a single direction – inputs are multiplied by weights to obtain
Feedforward_neural_network
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs
History of artificial neural networks
History_of_artificial_neural_networks
Machine-learning and computational-neuroscience conference
proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Hardware acceleration unit for artificial intelligence tasks
designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision. NPU can be standalone
Neural_processing_unit
Artificial Intelligence. Oxford, England: Oneworld Publications.[page needed] Tedesco, B. G. (1992), Neural Analysis: Artificial Intelligence Neural Networks Applied
Artificial intelligence in marketing
Artificial_intelligence_in_marketing
Academic journal
Transactions on Neural Networks and Learning Systems is a monthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society.
IEEE Transactions on Neural Networks and Learning Systems
IEEE_Transactions_on_Neural_Networks_and_Learning_Systems
Machine learning-powered structure design
Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine
Neural_architecture_search
Influential 2012 deep convolutional neural network
AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance in
AlexNet
Subfield of artificial intelligence
Neuro-symbolic AI is a subfield of artificial intelligence that combines neural networks and symbolic AI approaches, such as knowledge representation and
Neuro-symbolic_AI
Open-source artificial intelligence ecosystem
The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations
Open_Neural_Network_Exchange
German computer scientist (born 1963)
intelligence, specifically artificial neural networks. He has been described by media outlets as a leading pioneer of modern artificial intelligence.
Jürgen_Schmidhuber
Image identification technology
to be automatically censored. As of 2021, Apple was thought to be using NeuralHash for similar purposes. In 2022, The New York Times covered the story
PhotoDNA
Academic journal
Neural Networks is a monthly peer-reviewed scientific journal and an official journal of the International Neural Network Society, European Neural Network
Neural_Networks_(journal)
Neural-net machine
The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural network machine designed by Marvin Minsky. Prompted by a letter from Minsky,
Stochastic Neural Analog Reinforcement Calculator
Stochastic_Neural_Analog_Reinforcement_Calculator
Form of artificial intelligence
neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. It
Neuroevolution
Statistical law in machine learning
In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up
Neural_scaling_law
Topics referred to by the same term
national identification number used in France Neural Network Intelligence, an open source AutoML toolkit for neural architecture search and hyper-parameter
NNI
Methods in artificial intelligence research
and both are required for intelligence. Olazaran, in his sociological history of the controversies within the neural network community, described the moderate
Symbolic artificial intelligence
Symbolic_artificial_intelligence
artificial neural network or neural assembly that does not correspond to any previously learned patterns. The same term is also applied to the human neural mistake-making
Confabulation (neural networks)
Confabulation_(neural_networks)
Deep learning method
developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's
Generative adversarial network
Generative_adversarial_network
Classification of Artificial Neural Networks (ANNs)
Types of neural networks (NN) include a family of techniques. The simplest types have static components, including number of units, number of layers,
Types of artificial neural networks
Types_of_artificial_neural_networks
Public availability of the learned parameters of an artificial intelligence model
parameters of a trained artificial intelligence model, principally its weights and biases. In an artificial neural network, weights are numerical values that
Open_weights
Type of activation function
In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the
Rectified_linear_unit
Process of automating the application of machine learning
Artificial intelligence Artificial intelligence and elections Neural architecture search Neuroevolution Self-tuning Neural Network Intelligence ModelOps
Automated_machine_learning
Technique in artificial intelligence
Feedback neural networks are neural networks with the ability to provide bottom-up and top-down design feedback to their input or previous layers, based
Feedback_neural_network
AI that generates content
in the 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the
Generative_AI
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is
Transformer_(deep_learning)
Memory unit used in neural networks
In artificial neural networks, the gated recurrent unit (GRU) is a gating mechanism used in recurrent neural networks, introduced in 2014 by Kyunghyun
Gated_recurrent_unit
Semantic neural network (SNN) is based on John von Neumann's neural network [von Neumann, 1966] and Nikolai Amosov M-Network. There are limitations to
Semantic_neural_network
Overview of and topical guide to deep learning
learning is a subfield of machine learning and artificial intelligence based on artificial neural networks with multiple processing layers. It emphasizes representation
Outline_of_deep_learning
Computer system simulating intelligence
particular deep convolutional neural networks. Nowadays, deep learning has become the core method for artificial intelligence. In fact, some of the most
Computational_intelligence
List of academic journals in artificial intelligence
Computation – IEEE IEEE Transactions on Neural Networks and Learning Systems – IEEE Nature Machine Intelligence – Springer Nature International Journal
List of artificial intelligence journals
List_of_artificial_intelligence_journals
Approach to artificial intelligence
In the field of artificial intelligence, the designation neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-fuzzy
Neuro-fuzzy
Parallel computing paradigm
learning, Cellular Neural Networks (CNN) or Cellular Nonlinear Networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference
Cellular_neural_network
Graphical and scripting software
Nengo is a tool for modelling neural networks with applications in cognitive science, psychology, artificial intelligence and neuroscience. Some form of
Neural_Engineering_Object
Technique for setting initial values of trainable parameters in a neural network
parameter initialization describes the initial step in creating a neural network. A neural network contains trainable parameters that are modified during training:
Weight_initialization
The European Neural Network Society (ENNS) is an association of scientists, engineers, students, and others seeking to learn about and advance understanding
European Neural Network Society
European_Neural_Network_Society
British-Canadian computer scientist (born 1947)
psychologist and Nobel Prize laureate known for his work on artificial neural networks, which earned him the title "the Godfather of AI". He is University
Geoffrey_Hinton
Type of artificial neural network architecture
Kolmogorov–Arnold Networks (KANs) are a type of artificial neural network architecture inspired by the Kolmogorov–Arnold representation theorem, also
Kolmogorov–Arnold_Networks
Programming library
Fast Artificial Neural Network (FANN) is cross-platform programming library for developing multilayer feedforward artificial neural networks (ANNs). It is
Fast Artificial Neural Network
Fast_Artificial_Neural_Network
Quantum Mechanics in Neural Networks
Quantum neural networks are computational neural network models which are based on the principles of quantum mechanics. The first ideas on quantum neural computation
Quantum_neural_network
System developed by Google to increase fluency and accuracy in Google Translate
November 2016 that used an artificial neural network to increase fluency and accuracy in Google Translate. The neural network consisted of two main blocks, an
Google Neural Machine Translation
Google_Neural_Machine_Translation
Reverse-engineering neural networks
research within explainable artificial intelligence that aims to understand the internal workings of neural networks by analyzing their concrete structures
Mechanistic_interpretability
Association for Artificial Intelligence European Laboratory for Learning and Intelligent Systems European Neural Network Society German Research Centre
List of artificial intelligence institutions
List_of_artificial_intelligence_institutions
Mathematical function conceived as a crude model
of a biological neuron in a neural network. The artificial neuron is the elementary unit of an artificial neural network. The design of the artificial
Artificial_neuron
Visual programming tool to teach coding
Image Composite Editor Infer.NET LightGBM LiveStation MyLifeBits Neural Network Intelligence NodeXL OneFuzz PhotoDNA SEAL SLAM T2 Temporal Prover WorldWide
Kodu_Game_Lab
challenges players to draw a picture of an object or idea and then uses a neural network to guess what the drawing is. The Samuel Checkers-playing Program (1959)
List of artificial intelligence projects
List_of_artificial_intelligence_projects
List of concepts in artificial intelligence
designed as hardware acceleration for artificial intelligence applications, especially artificial neural networks, machine vision, and machine learning. AI-complete
Glossary of artificial intelligence
Glossary_of_artificial_intelligence
Analyzing AI systems by removing parts
organism), and is particularly used in the analysis of artificial neural networks by analogy with ablative brain surgery. Other analogies include other
Ablation (artificial intelligence)
Ablation_(artificial_intelligence)
Canadian computer scientist
Canadian computer scientist most noted for his work on artificial neural networks and deep learning. In 2012, Krizhevsky, Ilya Sutskever and their PhD
Alex_Krizhevsky
Machine learning framework
neural networks, marking a departure from the typical focus on learning mappings between finite-dimensional Euclidean spaces or finite sets. Neural operators
Neural_operators
Regularization method for artificial neural networks
a regularization technique for reducing overfitting in artificial neural networks by preventing complex co-adaptations on training data. The technique
Dropout_(neural_networks)
Artificial neural network
Neural gas is an artificial neural network, inspired by the self-organizing map and introduced in 1991 by Thomas Martinetz and Klaus Schulten. The neural
Neural_gas
Neuromorphic tech company
deployment of spiking neural networks (SNN), and the AKD1000 neuromorphic processor, a hardware implementation of their spiking neural network system. BrainChip's
BrainChip
Machine learning model family
Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and
Region Based Convolutional Neural Networks
Region_Based_Convolutional_Neural_Networks
Method by which information is represented in the brain
relationships among networks of neurons in an ensemble. Action potentials, which act as the primary carrier of information in biological neural networks, are generally
Neural_coding
Hardware specially designed and optimized for artificial intelligence
intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks
Hardware for artificial intelligence
Hardware_for_artificial_intelligence
Transactions on Pattern Analysis and Machine Intelligence – Neural Networks (journal) – On Intelligence – Paradigms of AI Programming: Case Studies in
Outline of artificial intelligence
Outline_of_artificial_intelligence
Subset of artificial intelligence
explicitly programmed. Advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine
Machine_learning
2021 64-bit mainframe microprocessor by IBM
instructions. The Neural Network Processing Assists (NNPA) instruction performs a variety of tensor instructions useful for neural networks. Telum II adds
IBM_Telum
American computer scientist, Microsoft vice president
learning; multimedia and graphics, security, search, gaming, networking, artificial intelligence and human-computer interaction. His team has collaborated
Richard_Rashid
Statistical model of language
texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical
Language_model
Recurrent neural network architecture
Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional
Long_short-term_memory
Type of recurrent neural network
A neural Turing machine (NTM) is a recurrent neural network model of a Turing machine. The approach was published by Alex Graves et al. in 2014. NTMs
Neural_Turing_machine
Neural network architecture
Time delay neural network (TDNN) is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance
Time_delay_neural_network
Quesada, Alberto (October 28, 2019). "5 algorithms to train a neural network". Neural Designer Blog. Artelnics. Retrieved April 20, 2026. Silver, David;
List of artificial intelligence algorithms
List_of_artificial_intelligence_algorithms
Type of feedforward neural network
learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation functions
Multilayer_perceptron
Microsoft Cognitive Toolkit MindsDB MindSpore ML.NET Neural Designer Neural Network Intelligence oneAPI OpenNN PlaidML PyTorch QLattice Scikit-learn Shogun
List_of_data_science_software
Neural Designer is a software tool for machine learning based on neural networks, a main area of artificial intelligence research, and contains a graphical
Neural_Designer
Integrated circuit technology
neural networks using error backpropagation. Neuromemristive systems use memristors to implement neuroplasticity, focusing on abstract neural network
Neuromorphic_computing
Subfield of machine learning
task space and facilitate problem solving. Siamese neural network is composed of two twin networks whose output is jointly trained. There is a function
Meta-learning (computer science)
Meta-learning_(computer_science)
Research field in deep learning
Traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in processing data on regular grids
Topological_deep_learning
American psychologist (1928–1971)
field of artificial intelligence. He is sometimes called the father of deep learning for his pioneering work on artificial neural networks. Rosenblatt was
Frank_Rosenblatt
Type of machine learning model
A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially
Large_language_model
Technology developed by Microsoft
Image Composite Editor Infer.NET LightGBM LiveStation MyLifeBits Neural Network Intelligence NodeXL OneFuzz PhotoDNA SEAL SLAM T2 Temporal Prover WorldWide
Deep_Zoom
Public document sharing service from Microsoft
Image Composite Editor Infer.NET LightGBM LiveStation MyLifeBits Neural Network Intelligence NodeXL OneFuzz PhotoDNA SEAL SLAM T2 Temporal Prover WorldWide
Docs.com
Research division of Microsoft
campus focuses on areas such as theory, artificial intelligence, machine learning, systems and networking, security, privacy, human–computer interaction,
Microsoft_Research
Machine learning technique
Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model of the
Transfer_learning
Form of artificial neural network
A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory
Hopfield_network
Types of approximate algorithm
recognition. Between the 1980s and 1990s, hybrid intelligence systems merged fuzzy logic, neural networks, and evolutionary computation that solved complicated
Soft_computing
Neural network technology
In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers
Convolutional_layer
AI's tendency to abruptly and drastically forget old info after learning new info
artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important
Catastrophic_interference
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE
NEURAL NETWORK-INTELLIGENCE