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Type of feedforward neural network
processing, standard convolutional layers can be replaced by depthwise separable convolutional layers, which are based on a depthwise convolution followed by a
Convolutional_neural_network
Neural network technology
neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the
Convolutional_layer
Convolutional neural network structure
motifs of modern convolutional neural networks, such as convolutional layer, pooling layer and full connection layer. Every convolutional layer includes three
LeNet
Explainable AI technique
classification, in convolutional neural networks (CNNs). These methods generate heatmaps by weighting the feature maps from a convolutional layer according to
Class_activation_mapping
Deep learning model structure
homogeneity. Deep Learning Neocortex § Layers "CS231n Convolutional Neural Networks for Visual Recognition". CS231n Convolutional Neural Networks for Visual Recognition
Layer_(deep_learning)
Concept in machine learning
neural networks allows tensors to express the convolution layers of a neural network. A convolutional layer has multiple inputs, each of which is a spatial
Tensor_(machine_learning)
Influential 2012 deep convolutional neural network
eight layers: the first five are convolutional layers, some of them followed by max-pooling layers, and the last three are fully connected layers. The
AlexNet
Type of artificial neural network
consists of three sequential convolutional layers and a residual connection. The first layer in this block is a 1×1 convolution for dimension reduction (e
Residual_neural_network
Series of convolutional neural networks for image classification
modules: Convolutional modules: 3 × 3 {\displaystyle 3\times 3} convolutional layers with stride 1, followed by ReLU activations. Max-pooling layers: After
VGGNet
Type of feedforward neural network
every neuron in one layer connects to every neuron in the next, from architectures with sparse connectivity such as convolutional neural networks. While
Multilayer_perceptron
Class of artificial neural networks
graph convolutional networks and graph attention networks, whose definitions can be expressed in terms of the MPNN formalism. The graph convolutional network
Graph_neural_network
Family of convolutional neural networks
factorized convolutions help. It also uses a form of dimension-reduction by concatenating the output from a convolutional layer and a pooling layer. As an
Inception (deep learning architecture)
Inception_(deep_learning_architecture)
Machine learning technique
per-channel BatchNorm. Concretely, suppose we have a 2-dimensional convolutional layer defined by:xh,w,c(l)=∑h′,w′,c′Kh′−h,w′−w,c,c′(l)xh′,w′,c′(l−1)+bc(l)where:
Normalization (machine learning)
Normalization_(machine_learning)
Computational model used in machine learning
neuron in one layer connecting to every neuron in the next layer. However, in convolutional neural networks, some layers are convolutional, meaning each
Neural network (machine learning)
Neural_network_(machine_learning)
Architectural motif in neural networks for aggregating information
the receptive field of neurons in later layers in the network. Pooling is most commonly used in convolutional neural networks (CNN). Below is a description
Pooling_layer
Branch of machine learning
Deep learning architectures for convolutional neural networks (CNNs) with convolutional layers and downsampling layers began with the Neocognitron introduced
Deep_learning
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
U-Net
science, a convolutional deep belief network (CDBN) is a type of deep artificial neural network composed of multiple layers of convolutional restricted
Convolutional deep belief network
Convolutional_deep_belief_network
Technique in neural networks for learning joint representations of text and images
3x3 convolutions instead of a single 7×7 convolution. There is an average pooling of stride 2 at the start of each downsampling convolutional layer (they
Contrastive language–image pre-training
Contrastive_language–image_pre-training
Type of error-correcting code using convolution
represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'. The sliding nature of the convolutional codes facilitates
Convolutional_code
Outer layer of the cerebrum of the mammalian brain
The cerebral cortex, also known as the cerebral mantle, is the outer layer of neural tissue of the cerebrum of the brain in humans and other mammals.
Cerebral_cortex
Object detection system
Once (YOLO) is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015, YOLO
You_Only_Look_Once
Involutive change of basis in linear algebra
convolution between two vectors is equivalent to element-wise multiplication of their Hadamard transform representations; thus convolutional layers can
Hadamard_transform
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
History of artificial neural networks
History_of_artificial_neural_networks
Neural network architecture
shift-invariance, and 2) model context at each layer of the network. It is essentially a 1-d convolutional neural network (CNN). Shift-invariant classification
Time_delay_neural_network
Diffusion model over latent embedding space
down-scaling layer in the backbone: The latent array and the time-embedding are processed by a ResBlock: The latent array is processed by a convolutional layer. The
Latent_diffusion_model
Type of artificial neural network
three layers, notable for being able to distinguish data that is not linearly separable. Examples of other feedforward networks include convolutional neural
Feedforward_neural_network
Delimited medium where some stimuli can evoke neuronal responses
output feature (of any layer) to the input region (patch). The idea of receptive fields applies to local operations (i.e. convolution, pooling). As an example
Receptive_field
Structure at the rear of the vertebrate brain, beneath the cerebrum
irregular convolutions of the cerebral cortex. These parallel grooves conceal the fact that the cerebellar cortex is actually a thin, continuous layer of tissue
Cerebellum
Deep learning facial recognition system
sequence of layers, arranged as follows: convolutional layer - max pooling - convolutional layer - 3 locally connected layers - fully connected layer. The input
DeepFace
for its simplicity and use of 3x3 convolution filters Inception — CNN architecture using parallel convolutional layers of different sizes Mamba – selective
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
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
Algorithm for modelling sequential data
and each token is converted into a vector through an embedding layer. At each layer, each token is then contextualized within the scope of the context
Transformer_(deep_learning)
Machine learning technique
architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen, as they capture
Fine-tuning_(deep_learning)
Dimensionality reduction of graph-based semantic data objects [machine learning task]
{[h;{\mathcal {r}};t]}}} and is used to feed to a convolutional layer to extract the convolutional features. These features are then redirected to a capsule
Knowledge_graph_embedding
Machine learning model for speech
spectrogram as input and processes it. It first passes through two convolutional layers. Sinusoidal positional embeddings are added. It is then processed
Whisper (speech recognition system)
Whisper_(speech_recognition_system)
Machine learning-powered structure design
multiple outputs at each layer. In the studied example, the best convolutional layer (or "cell") was designed for the CIFAR-10 dataset and then applied
Neural_architecture_search
Database of handwritten digits
single convolutional neural network best performance was 0.25 percent error rate. As of August 2018, the best performance of a single convolutional neural
MNIST_database
Type of artificial neural network
valuable. To achieve this, a capsnet's lower layers are convolutional, including hidden capsule layers. Higher layers thus cover larger regions, while retaining
Capsule_neural_network
Machine learning framework
uses only deep networks consisting entirely of convolution-deconvolution layers, that is, fully convolutional networks. Self-attention GAN (SAGAN): Starts
Generative adversarial network
Generative_adversarial_network
Method of speech synthesis that uses deep neural networks
pθ(𝐱)=∏t=1Tp(xt|x1,...,xt−1) where θ is the model parameter including many dilated convolution layers. Thus, each audio sample xt is conditioned on the samples at all previous
Deep learning speech synthesis
Deep_learning_speech_synthesis
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
Classification of Artificial Neural Networks (ANNs)
architecture is useful for basic pattern recognition. Convolutional neural networks (CNN): Uses convolutional filters (small sliding windows) to scan across
Types of artificial neural networks
Types_of_artificial_neural_networks
Computer Go program
Darkfmct3 uses a 12-layer full convolutional network with a width of 384 nodes without weight sharing or pooling. Each convolutional layer is followed by a
Darkforest
Interdisciplinary research area
the quantum convolutional filter are: the encoder, the parameterized quantum circuit (PQC), and the measurement. The quantum convolutional filter can be
Quantum_machine_learning
Deep neural network for generating raw audio
audio. WaveNet is a type of feedforward neural network known as a deep convolutional neural network (CNN). In WaveNet, the CNN takes a raw signal as an input
WaveNet
Artificial neural network node function
functions are extensively used in the pooling layers in convolutional neural networks, and in output layers of multiclass classification networks. These
Activation_function
January 2019. Fukushima, Kunihiko (2020-01-24). "Neocognitron: Deep Convolutional Neural Network". Institute of Electronics, Information and Communication
Timeline_of_machine_learning
Green's function for Laplacian
then the Newtonian potential of μ is referred to as a simple layer potential. Simple layer potentials are continuous and solve the Laplace equation except
Newtonian_potential
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
Neural network coding model
multi-layer extension of the model has shown conceptual benefits for more complex signal decompositions, as well as a tight connection the convolutional neural
Convolutional_sparse_coding
Mobile data technology for GSM networks
code and the puncturing rate of the convolutional code. In GPRS Coding Schemes CS-1 through CS-3, the convolutional code is of rate 1/2, i.e. each input
EDGE_(telecommunication)
Physical implementation of an artificial neural network with optical components
convolution operation kernels in this implementation are also fabricated phase masks, limiting the device's functionality to specific convolutional layers
Optical_neural_network
Top part of the skull
surface of the skull-cap is concave and presents depressions for the convolutions of the cerebrum, together with numerous furrows for the lodgement of
Calvaria_(skull)
Novel generative adversarial network
two ways. One, it applies the style latent vector to transform the convolution layer's weights instead, thus solving the "blob" problem. The "blob" problem
StyleGAN
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
Software program
program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic
DeepDream
Partitioning a digital image into segments
the traditional stack of convolutional and max pooling layers to increase the receptive field as it goes through the layers. It is used to capture the
Image_segmentation
frames in video analytics tasks, such as human action recognition. In convolutional neural network-based systems, the prioritization introduced by the attention
Visual_temporal_attention
Detecting small objects in digital images
while they pass through convolution networks." Crowd counting Vehicle re-identification Animal detection Fish detection Convolutional neural network Use of
Small_object_detection
Elementwise product of two matrices
can also be used in artificial neural network models, specifically convolutional layers. Frobenius inner product Pointwise product Kronecker product Khatri–Rao
Hadamard_product_(matrices)
Property of artificial neural networks
on graphs (or rather on graph isomorphism classes) by popular graph convolutional neural networks (GCNs or GNNs) can be made as discriminative as the
Universal approximation theorem
Universal_approximation_theorem
High-performance forward error correction codes
BCJR algorithm Convolutional code Forward error correction Interleaver Low-density parity-check code Serial concatenated convolutional codes Soft-decision
Turbo_code
Eigenvalue algorithm
Allauzen, A. (2023), "Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration", Proceedings of the 40th International Conference
Power_iteration
Deep learning architecture
(26 October 2021). "Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers". NeurIPS. S2CID 239998472. Tickoo, Aneesh
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Technique for setting initial values of trainable parameters in a neural network
generalized to layer-sequential unit-variance (LSUV) initialization. It is a data-dependent initialization method, and can be used in convolutional neural networks
Weight_initialization
the original FRAME model, Lu et al. uses the filters at a certain convolutional layer of a pre-learned ConvNet. Instead of relying on the pre-trained filters
Filters, random fields, and maximum entropy model
Filters,_random_fields,_and_maximum_entropy_model
Paradigm in machine learning that uses no classification labels
normally not considered a layer, but in the Helmholtz machine generation mode, the data layer receives input from the middle layer and has separate weights
Unsupervised_learning
Neural network that learns efficient data encoding in an unsupervised manner
Lazzaretti, André Eugênio; Lopes, Heitor Silvério (2018). "A study of deep convolutional auto-encoders for anomaly detection in videos". Pattern Recognition
Autoencoder
Image dataset
Krizhevsky, Alex (2009). "Learning Multiple Layers of Features from Tiny Images" (PDF). "Convolutional Deep Belief Networks on CIFAR-10" (PDF). Goodfellow
CIFAR-10
in a hierarchy, meaning that earlier convolutional layers will learn smaller local patterns while later layers will learn larger patterns based on the
Machine learning in video games
Machine_learning_in_video_games
Numerical technique
A perfectly matched layer (PML) is an artificial absorbing layer for wave equations, commonly used to truncate computational regions in numerical methods
Perfectly_matched_layer
Topics referred to by the same term
created to perform a number of tasks Pooling layer, a form of non-linear down-sampling in convolutional neural networks Pool (John Zorn album), 1980 Pool
Pool
address averse power line channel properties, robustness mechanism convolutional encoding (optional), scrambling and interleaving are used. PRIME Specification
PRIME (power-line communication)
PRIME_(power-line_communication)
Packet-oriented mobile data service for 2G and 3G networks
cyclic code and the puncturing rate of the convolutional code. In Coding Schemes CS-1 through CS-3, the convolutional code is of rate 1/2; i.e., each input
GPRS
Regulator for flow of signals in neural networks
control the flow of information through different channels inside a convolutional neural network (CNN). Gated Attention Unit (GAU) are used in attention
Gating_mechanism
Mammalian structure involved in higher-order brain functions
The neocortex, also called the neopallium, isocortex or six-layered cortex, is a set of layers of the mammalian cerebral cortex involved in higher-order
Neocortex
Greek computer vision and robotics researcher
creating regional representations of salient regions directly from convolutional layer activation. They found that their system has improved robustness
Margarita_Chli
multi-layered tissues," Computer Methods and Programs in Biomedicine 47, 131–146 (1995). L.-H. Wang, S. L. Jacques, and L.-Q. Zheng, "Convolution for responses
Convolution for optical broad-beam responses in scattering media
Convolution_for_optical_broad-beam_responses_in_scattering_media
2017 paper conceptualizing the transformer-based deep learning architecture
examples include the vision transformer, which has led to developments in convolutional neural networks. Image and video generators like DALL-E (2021), Stable
Attention_Is_All_You_Need
Type of visual artifact
because it utilizes a Convolutional Neural Network (CNN) to frontload image estimation and guide model parameter estimation. Convolutional Neural Networks leverage
MRI_artifact
Numerical methods for matrix eigenvalue calculation
Allauzen, A. (2023), "Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration", Proceedings of the 40th International Conference
Eigenvalue_algorithm
Software for understanding biological data
extraction makes CNNs a desirable model. A phylogenetic convolutional neural network (Ph-CNN) is a convolutional neural network architecture proposed by Fioranti
Machine_learning_in_biology
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
Research field in deep learning
non-Euclidean data structures. Traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in
Topological_deep_learning
Distribution over functions corresponding to an infinitely wide Bayesian neural network
Single hidden layer Bayesian neural networks; deep fully connected networks as the number of units per layer is taken to infinity; convolutional neural networks
Neural network Gaussian process
Neural_network_Gaussian_process
Series of language models developed by Google AI
("global attention"), while other layers use only a sliding-window attention of the nearest 128 tokens, like convolutional neural network ("local attention")
BERT_(language_model)
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
Type of software algorithm for image manipulation
of a single convolutional neural network (CNN) on two images. The style similarity is the weighted sum of Gram matrices within each layer (see below for
Neural_style_transfer
Recurrent neural network architecture
the sigmoid function) to a weighted sum. Peephole convolutional LSTM. The ∗ denotes the convolution operator. ft=σg(Wf∗xt+Uf∗ht−1+Vf⊙ct−1+bf)it=σg(Wi
Long_short-term_memory
Concept on molecular modeling
chemical model. SchNet a Neural network utilising continuous-filter convolutional layers, to predict chemical properties and potential energy surfaces. PhysNet
Force_field_(chemistry)
Method that extracts features from radiographic medical images
learning methods such as convolutional neural networks that learn features automatically from the data in convolutional layers and then make predictions
Radiomics
Overview of and topical guide to deep learning
Sigmoid function Softmax function Embedding Convolution Pooling layer Attention Batch normalization Layer normalization Residual connections Backpropagation
Outline_of_deep_learning
American electrical engineer
development of the Uniaxial Perfectly Matched Layer media method, the complex-frequency shifted convolutional PML, along with J. Alan Roden, and his contributions
Stephen_Gedney
style convolutional neural network. The encoder-decoder network was made of residual convolution, upsampling, and high field-of-view convolution modules
Deep learning in photoacoustic imaging
Deep_learning_in_photoacoustic_imaging
Machine learning technique
be overworked. Since the inputs cannot move through the layer until every expert in the layer has finished the queries it is assigned, load balancing
Mixture_of_experts
Topics referred to by the same term
an industry standard used in wholesale energy trading Convolutional Perfectly Matched Layer, a grid truncation technique in the finite-difference time-domain
CPML
Type of artificial neural network
composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained without
Deep_belief_network
First CDMA-based digital cellular technology
is divided into frames of bits. A frame of bits is passed through a convolutional encoder, adding forward error correction redundancy, generating a frame
CdmaOne
Machine learning technique
Some class-sensitive interpretability methods originally developed for convolutional neural networks can be also applied to ViT, such as GradCAM, which back-propagates
Attention_(machine_learning)
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CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
CONVOLUTIONAL LAYER
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