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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 networks
certain existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs. A convolutional neural network layer, in the
Graph_neural_network
Branch of machine learning
networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance
Deep_learning
Neural network technology
artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are
Convolutional_layer
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)
recurrent neural networks and convolutional neural networks, renewed interest in ANNs. The 2010s saw the development of a deep neural network (i.e., one
History of artificial neural networks
History_of_artificial_neural_networks
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
Software program
created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia
DeepDream
Convolutional neural network structure
LeNet is a series of convolutional neural network architectures created by a research group at AT&T Bell Laboratories between of the period of 1988 to
LeNet
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
Type of artificial neural network
closely mimic biological neural organization. The idea is to add structures called "capsules" to a convolutional neural network (CNN), and to reuse output
Capsule_neural_network
Neural network architecture
and 2) model context at each layer of the network. It is essentially a 1-d convolutional neural network (CNN). Shift-invariant classification means
Time_delay_neural_network
Object detection system
is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015, YOLO has
You_Only_Look_Once
Database of handwritten digits
convolutional neural network best performance was 0.25 percent error rate. As of August 2018, the best performance of a single convolutional neural network
MNIST_database
Computer scientist (born 1986)
Alex Krizhevsky and Geoffrey Hinton, he co-created AlexNet, a convolutional neural network. One of the most highly cited computer scientists in history
Ilya_Sutskever
April 2026. Dhillon, Anamika; Verma, Gyanendra K. (2020-06-01). "Convolutional neural network: a review of models, methodologies and applications to object
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
Series of convolutional neural networks for image classification
The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family
VGGNet
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
Type of convolutional neural network
U-Net is a convolutional neural network that was developed for image segmentation. The network is based on a fully convolutional neural network whose architecture
U-Net
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
Type of artificial neural network
physics-informed neural networks. Differently from traditional machine learning algorithms, such as feed-forward neural networks, convolutional neural networks, or
Neural_field
Family of computer vision models designed for efficient inference on mobile devices
MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision
MobileNet
Neural network working on two input vectors
A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on
Siamese_neural_network
Video coding format
In the VC-6 standard an up-sampler developed with an in-loop Convolutional Neural Network is provided to optimize the detail in the reconstructed image
VC-6
Dimensionality reduction of graph-based semantic data objects [machine learning task]
Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network". Proceedings of the 2018 Conference of the North American Chapter
Knowledge_graph_embedding
Trimming artificial neural networks to reduce computational overhead
convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440. Gildenblat, Jacob (2017-06-23). "Pruning deep neural
Pruning (artificial neural network)
Pruning_(artificial_neural_network)
Classification of Artificial Neural Networks (ANNs)
has enabled neural networks to reach the general public via chatbots, code generators and many other forms. Convolutional neural networks (CNN): a FNN
Types of artificial neural networks
Types_of_artificial_neural_networks
Parallel computing paradigm
other sensory-motor organs. CNN is not to be confused with convolutional neural networks (also colloquially called CNN). Due to their number and variety
Cellular_neural_network
Algorithm for noise reduction in images
of the two objective functions. An approach that integrates a convolutional neural network has been proposed and shows better results (albeit with a slower
Block-matching and 3D filtering
Block-matching_and_3D_filtering
Deep learning method
generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network. GANs are implicit generative models, which
Generative adversarial network
Generative_adversarial_network
Neural network based evaluation function
NNUE, which stands for efficiently updatable neural network (often stylized as ƎUИИ) is a neural network made to replace the evaluation of Shogi, chess
Efficiently updatable neural network
Efficiently_updatable_neural_network
Type of activation function
called "positive part") was critical for object recognition in convolutional neural networks (CNNs), specifically because it allows average pooling without
Rectified_linear_unit
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
Continuous generalization of cellular automata
special case of recurrent convolutional neural networks. Lenia's update rule may also be seen as a single-layer convolution (the "potential field" K {\displaystyle
Lenia
Computerized information extraction from images
of a Convolutional Neural Network". Neurocomputing. 407: 439–453. doi:10.1016/j.neucom.2020.04.018. S2CID 219470398. Convolutional neural networks (CNNs)
Computer_vision
Class of artificial neural network
Multilingual Language Processing. Also, LSTM combined with convolutional neural networks (CNNs) improved automatic image captioning. The idea of encoder-decoder
Recurrent_neural_network
Integral expressing the amount of overlap of one function as it is shifted over another
of a Convolutional Neural Network". Neurocomputing. 407: 439–453. doi:10.1016/j.neucom.2020.04.018. S2CID 219470398. Convolutional neural networks represent
Convolution
Distribution over functions corresponding to an infinitely wide Bayesian neural network
Bayesian neural networks; deep fully connected networks as the number of units per layer is taken to infinity; convolutional neural networks as the number
Neural network Gaussian process
Neural_network_Gaussian_process
Image scaling algorithm
other types of photos. waifu2x was inspired by Super-Resolution Convolutional Neural Network (SRCNN). It uses Nvidia's CUDA for computing, although alternative
Waifu2x
Interdisciplinary research area
Generators (QRNGs) to machine learning models including Neural Networks and Convolutional Neural Networks for random initial weight distribution and Random
Quantum_machine_learning
Machine learning technique
the model's weights frozen. For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the
Fine-tuning_(deep_learning)
Physical implementation of an artificial neural network with optical components
An optical neural network is a physical implementation of an artificial neural network with optical components. Early optical neural networks used a photorefractive
Optical_neural_network
Family of convolutional neural networks
Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed
Inception (deep learning architecture)
Inception_(deep_learning_architecture)
Family of computer vision models
EfficientNet is a family of convolutional neural networks (CNNs) for computer vision published by researchers at Google AI in 2019. Its key innovation
EfficientNet
Software for understanding biological data
CNNs a desirable model. A phylogenetic convolutional neural network (Ph-CNN) is a convolutional neural network architecture proposed by Fioranti et al
Machine learning in bioinformatics
Machine_learning_in_bioinformatics
British machine learning academic (born 1971)
"Teaching Deep Convolutional Neural Networks to Play Go". Convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly
Amos_Storkey
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
Topological_deep_learning
Type of neural network which utilizes recursion
include graph neural network (GNN), Neural Network for Graphs (NN4G), and more recently convolutional neural networks for graphs. Goller, C.; Küchler, A
Recursive_neural_network
Type of artificial neural network
for convolutional neural networks. Previously in 1969, he published a similar architecture, but with hand-designed kernels inspired by convolutions in
Neocognitron
Architectural motif in neural networks for aggregating information
field of neurons in later layers in the network. Pooling is most commonly used in convolutional neural networks (CNN). Below is a description of pooling
Pooling_layer
Involutive change of basis in linear algebra
machine learning, particularly in hybrid quantum-classical neural networks. Dyadic convolution between two vectors is equivalent to element-wise multiplication
Hadamard_transform
Paradigm in machine learning that uses no classification labels
large-scale unsupervised learning has been done by training general-purpose neural network architectures by gradient descent, adapted to performing unsupervised
Unsupervised_learning
Machine learning paradigm
sample pairs. An early example uses a pair of 1-dimensional convolutional neural networks to process a pair of images and maximize their agreement. Contrastive
Self-supervised_learning
Regularization method for artificial neural networks
currently holds the patent for the dropout technique. AlexNet Convolutional neural network § Dropout The patent is most likely not valid due to previous
Dropout_(neural_networks)
Computer system simulating intelligence
be regarded as parts of CI: Fuzzy systems Neural networks and, in particular, convolutional neural networks Evolutionary computation and, in particular
Computational_intelligence
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)
Technique for setting initial values of trainable parameters in a neural network
neural network as trainable parameters, so this article describes how both of these are initialized. Similarly, trainable parameters in convolutional
Weight_initialization
type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly
Deep_image_prior
Approach in generative models
generative neural network is the generative ConvNet proposed in 2016 for image patterns, where the neural network is a convolutional neural network. The model
Energy-based_model
Machine learning model for vision processing
started with a ResNet, a standard convolutional neural network used for computer vision, and replaced all convolutional kernels by the self-attention mechanism
Vision_transformer
Interpretable computational sub-graphs within artificial neural networks
artificial neural networks, initially focusing on convolutional neural networks (CNNs) used in vision models: Features are the fundamental unit of networks: Rather
Circuit_(neural_network)
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
Data analysis technique
representation of the minority class, improving model performance. When convolutional neural networks grew larger in mid-1990s, there was a lack of data to use, especially
Data_augmentation
Concept in machine learning
Fully Convolutional Nets with a Single High-Order Tensor". arXiv:1904.02698 [cs.CV]. Lebedev, Vadim (2014), Speeding-up Convolutional Neural Networks Using
Tensor_(machine_learning)
Set of learning techniques in machine learning
to many modalities through the use of deep neural network architectures such as convolutional neural networks and transformers. Supervised feature learning
Feature_learning
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
Machine learning technique
signals. The experiments noted that the accuracy of neural networks and convolutional neural networks were improved through transfer learning both prior
Transfer_learning
Canadian computer scientist
classification. Building on Convolutional Neural Networks and Sutskever’s Deep Neural Network approach of deepening the neural layers far beyond the convention
Alex_Krizhevsky
Facial recognition system
Computer Vision and Pattern Recognition. The system uses a deep convolutional neural network to learn a mapping (also called an embedding) from a set of face
FaceNet
Machine translation using artificial neural networks
using a convolutional neural network (CNN) for encoding the source and both Cho et al. and Sutskever et al. using a recurrent neural network (RNN) instead
Neural_machine_translation
and run on. Convolutional neural networks (CNN) are specialized ANNs that are often used to analyze image data. These types of networks are able to learn
Machine learning in video games
Machine_learning_in_video_games
Approach in data analysis
With the advent of deep learning technologies, methods using Convolutional Neural Networks (CNNs) and Simple Recurrent Units (SRUs) have shown significant
Anomaly_detection
Hardware acceleration unit for artificial intelligence tasks
accelerators specialized for machine vision algorithms such as CNN (convolutional neural networks) and SIFT (scale-invariant feature transform). They are used
Neural_processing_unit
Deep learning library
Convolutional Architecture for Fast Feature Embedding (Caffe2), but models defined by the two frameworks were mutually incompatible. The Open Neural Network
PyTorch
Machine learning technique
positional attention and factorized positional attention. For convolutional neural networks, attention mechanisms can be distinguished by the dimension
Attention_(machine_learning)
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
Deep learning architecture
dependencies by combining the strengths of continuous-time, recurrent, and convolutional models, enabling it to handle irregularly sampled data, have unbounded
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
while simple linear iterative clustering convolutional neural network (SLIC-CNN) and convolutional neural networks (CNNs) are commonly applied to aerial
Machine learning in earth sciences
Machine_learning_in_earth_sciences
Deep neural network for image classification, released 2016
including semantic segmentation of images and style transfer. Convolutional neural network MobileNet EfficientNet You Only Look Once Edge computing Iandola
SqueezeNet
Image dataset
classes and 19,737 images (in 2010). On 30 September 2012, a convolutional neural network (CNN) called AlexNet achieved a top-5 error of 15.3% in the ImageNet
ImageNet
American computer scientist
machine learning, he is known for the Time Delay Neural Network (TDNN), the first Convolutional Neural Network (CNN) trained by gradient descent, using backpropagation
Alex_Waibel
Artificial neural network node function
used in the pooling layers in convolutional neural networks, and in output layers of multiclass classification networks. These activations perform aggregation
Activation_function
French computer scientist (born 1960)
on optical character recognition and computer vision using convolutional neural networks (CNNs). He is also one of the main creators of the DjVu image
Yann_LeCun
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
Deep learning model structure
Neocortex § Layers "CS231n Convolutional Neural Networks for Visual Recognition". CS231n Convolutional Neural Networks for Visual Recognition. 10 May 2016. Retrieved
Layer_(deep_learning)
Czechoslovak-born AI researcher (born 1986)
instructor of the first deep learning course at Stanford, CS 231n: Convolutional Neural Networks for Visual Recognition. The course became one of the largest
Andrej_Karpathy
Model-free reinforcement learning algorithm
human levels. The DeepMind system used a deep convolutional neural network, with layers of tiled convolutional filters to mimic the effects of receptive fields
Q-learning
Type of software algorithm for image manipulation
weighted sum of squared-differences between the neural activations of a single convolutional neural network (CNN) on two images. The style similarity is
Neural_style_transfer
Image dataset
students were paid to label all of the images. Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10. This
CIFAR-10
Hypothesis in machine learning
proven for the special case of convolutional neural networks. Grokking (machine learning) Pruning (artificial neural network) Frankle, Jonathan; Carbin,
Lottery_ticket_hypothesis
Technique in neural networks for learning joint representations of text and images
Classification with Convolutional Neural Networks". arXiv:1812.01187 [cs.CV]. Zhang, Richard (2018-09-27). "Making Convolutional Networks Shift-Invariant
Contrastive Language–Image Pre-training
Contrastive_Language–Image_Pre-training
Computer graphics anti-aliasing algorithm
feeds into a convolutional neural network that processes the image to reduce aliasing while preserving fine detail. The neural network architecture employs
Deep_Learning_Anti-Aliasing
Combining of sensor data from disparate sources
and algorithms, including: Kalman filter Bayesian networks Dempster–Shafer Convolutional neural network Gaussian processes Two example sensor fusion calculations
Sensor_fusion
Device that selects between several analog or digital input signals
multiply-accumulate operation, demonstrating feasibility in accelerating convolutional neural network on field-programmable gate arrays. Digital subscriber line access
Multiplexer
Dex-net is a robotic. It uses a Grasp Quality Convolutional Neural Network to learn how to grasp unusually shaped objects. Dex-net was developed by University
DexNet
Reverse-engineering neural networks
workings of neural networks by analyzing their concrete structures, algorithms and circuits. This approach seeks to analyze neural networks in a manner
Mechanistic_interpretability
Japanese computer scientist (born 1936)
1980, Fukushima published the neocognitron, the original deep convolutional neural network (CNN) architecture. Fukushima proposed several supervised and
Kunihiko_Fukushima
Software library for natural language processing
learning library Thinc. Using Thinc as its backend, spaCy features convolutional neural network models for part-of-speech tagging, dependency parsing, text categorization
SpaCy
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
Form of artificial intelligence
intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. It is most commonly applied in artificial
Neuroevolution
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CONVOLUTIONAL NEURAL-NETWORK
CONVOLUTIONAL NEURAL-NETWORK
CONVOLUTIONAL NEURAL-NETWORK
CONVOLUTIONAL NEURAL-NETWORK
CONVOLUTIONAL NEURAL-NETWORK
CONVOLUTIONAL NEURAL-NETWORK