Statements (402)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
gptkb:Artificial_Intelligence gptkb:neural_networks gptkb:machine_learning gptkb:computer gptkb:networking |
| gptkbp:allows |
Variable Input Sizes
|
| gptkbp:analyzes |
Filters
Feature Maps Activation Maps |
| gptkbp:applies_to |
Convolution Operation
Max Pooling Re LU Activation Upsampling Techniques |
| gptkbp:architecture |
gptkb:AI_technology
|
| gptkbp:based_on |
Convolutional Layers
Pooling Layers Activation Functions |
| gptkbp:benefits |
Captures Graph Structure
Handles Irregular Data Scalable to Large Graphs |
| gptkbp:challenges |
Scalability Issues
Overfitting Underfitting Exploding Gradient Problem Vanishing Gradient Problem Data Sparsity Training Complexity Lack of Interpretability |
| gptkbp:characteristic |
feedback connections
memory capability |
| gptkbp:characterized_by |
Local Connectivity
Parameter Sharing Hierarchical Feature Learning |
| gptkbp:collaborated_with |
Academic Institutions
Industry Partners |
| gptkbp:competes_with |
gptkb:Google_Tensor
gptkb:Qualcomm_AI_Engine |
| gptkbp:designed_for |
gptkb:Artificial_Intelligence
gptkb:machine_learning |
| gptkbp:developed_by |
gptkb:wearable_technology
gptkb:Yoshua_Bengio gptkb:Max_Welling gptkb:Apple gptkb:Google gptkb:vehicles gptkb:Thomas_Kipf gptkb:David_Rumelhart gptkb:financial_services gptkb:Geoffrey_R._Hinton gptkb:Yann_Le_Cun gptkb:drones gptkb:robotics gptkb:smart_home_devices gptkb:Jonathan_Long Research Institutions data analysis Tech Companies automation computer vision personalization energy management security systems speech recognition transportation systems AI applications recommendation systems health monitoring virtual assistants automated customer service smart cameras predictive text gaming AI |
| gptkbp:enables |
image recognition
natural language processing facial recognition AR experiences |
| gptkbp:enhances |
Data Processing
AI performance Siri functionality camera features |
| gptkbp:features |
Parallel Processing
|
| gptkbp:first_introduced |
gptkb:1989
gptkb:i_Phone_8 |
| gptkbp:has |
Input Layer
Output Layer Hidden Layers |
| gptkbp:has_applications_in |
gptkb:Augmented_Reality
gptkb:Autonomous_Vehicles gptkb:Computer_Vision gptkb:Natural_Language_Processing gptkb:vehicles gptkb:Expert_Systems gptkb:speeches gptkb:Data_Analytics gptkb:robotics Autonomous Driving Image Recognition Object Detection Recommendation Systems Social Network Analysis Facial Recognition Medical Imaging healthcare gaming Game Playing Video Analysis Traffic Prediction Satellite Image Analysis Molecular Chemistry |
| gptkbp:has_component |
gptkb:Brain
gptkb:Weight Activation Function Layer Layers Activation Functions Bias Weights |
| gptkbp:has_field |
gptkb:Artificial_Intelligence
Computer Science |
| gptkbp:has_impact_on |
gptkb:transportation
gptkb:Education gptkb:economy gptkb:society Healthcare |
| gptkbp:has_limitations |
Computationally Intensive
Data Quality Overfitting Underfitting Interpretability Requires Labeled Data Sensitive to Input Variations Requires Large Datasets vanishing gradient problem exploding gradient problem Sensitive to Overfitting |
| gptkbp:has_type |
gptkb:Ganon
gptkb:GCN gptkb:Radial_Basis_Function_Network gptkb:Feedforward_Neural_Network gptkb:neural_networks gptkb:Deep_Learning gptkb:Gated_Recurrent_Unit_(GRU) gptkb:Long_Short-Term_Memory_(LSTM) gptkb:Cheb_Net gptkb:Graph_SAGE GAT GNN Graph Attention Network Message Passing Neural Network Spatial-Temporal Graph Neural Network |
| gptkbp:has_variants |
gptkb:neural_networks
gptkb:Mobile_Net gptkb:Dense_Net gptkb:Inception_Network gptkb:Res_Net gptkb:VGGNet |
| https://www.w3.org/2000/01/rdf-schema#label |
neural networks
|
| gptkbp:improves |
app performance
|
| gptkbp:influenced_by |
Biological Neural Networks
|
| gptkbp:input_output |
gptkb:Images
Segmentation Maps |
| gptkbp:integrates_with |
gptkb:cloud_services
|
| gptkbp:is_a |
Computational Model
|
| gptkbp:is_adopted_by |
gptkb:Augmented_Reality
gptkb:virtual_reality gptkb:robotics Facial Recognition |
| gptkbp:is_applied_in |
Self-Driving Cars
Facial Recognition Medical Imaging Video Analysis speech recognition video analysis image captioning music generation |
| gptkbp:is_associated_with |
gptkb:Augmented_Reality
gptkb:virtual_reality gptkb:cloud_computing gptkb:Internet_of_Things gptkb:Big_Data |
| gptkbp:is_challenged_by |
Privacy Concerns
Overfitting Bias Job Displacement Security Issues Vanishing Gradient Problem Ethical Dilemmas Data Augmentation Needs Label Noise |
| gptkbp:is_characterized_by |
Local Connectivity
Parameter Sharing Hierarchical Feature Learning |
| gptkbp:is_compared_to |
gptkb:Feedforward_Neural_Network
gptkb:Recurrent_Neural_Networks gptkb:Deep_Lab gptkb:FCN-8s gptkb:Mask_R-CNN gptkb:Seg_Net Traditional CNNs Fully Connected Networks |
| gptkbp:is_enhanced_by |
gptkb:stage_adaptation
Data Augmentation Ensemble Methods |
| gptkbp:is_evaluated_by |
gptkb:historical_memory
Accuracy F1 Score Performance Metrics Precision Recall Loss Function User Feedback Benchmark Tests Case Studies Research Publications F1 score accuracy confusion matrix precision AUC-ROC loss function Mean Pixel Accuracy Pixel Accuracy Intersection over Union (Io U) |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:MATLAB gptkb:Python gptkb:Keras gptkb:Caffe gptkb:Py_Torch DGL Py G Spektral |
| gptkbp:is_influenced_by |
gptkb:Cognitive_Computing
gptkb:neural_networks gptkb:Deep_Learning gptkb:VGGNet gptkb:Na'vi gptkb:psychology gptkb:Alex_Net Biological Neural Networks Cognitive Science Neuroscience biological neural networks cognitive science Visual Cortex |
| gptkbp:is_integrated_with |
gptkb:A-series_chips
gptkb:M-series_chips |
| gptkbp:is_known_for |
Real-time Processing
Feature Extraction Spatial Hierarchy End-to-End Learning |
| gptkbp:is_optimized_for |
gptkb:neural_networks
gptkb:i_OS gptkb:mac_OS |
| gptkbp:is_part_of |
gptkb:Artificial_Intelligence
gptkb:Apple gptkb:machine_learning gptkb:technology computational neuroscience |
| gptkbp:is_popular_in |
gptkb:Computer_Vision
gptkb:Artificial_Intelligence gptkb:Artificial_Neural_Networks gptkb:Big_Data gptkb:Deep_Learning gptkb:Data_Analytics gptkb:Data_Science gptkb:robotics Deep Learning Research deep learning community |
| gptkbp:is_promoted_by |
gptkb:Workshops
Conferences Webinars |
| gptkbp:is_regulated_by |
Government Policies
Ethical Guidelines |
| gptkbp:is_related_to |
gptkb:Statistician
gptkb:neural_networks gptkb:machine_learning gptkb:Decision_Tree gptkb:Random_Forest Feature Learning Semi-Supervised Learning Unsupervised Learning Graph Theory Deep Learning Frameworks Instance Segmentation Semantic Segmentation Link Prediction Node Classification Graph Representation Learning |
| gptkbp:is_supported_by |
gptkb:Tensor_Flow
gptkb:Keras gptkb:Caffe gptkb:stage_adaptation gptkb:Py_Torch gptkb:Dropout Regularization Techniques Funding Agencies Venture Capitalists Data Augmentation Techniques |
| gptkbp:is_trained_in |
gptkb:Adam_Optimizer
gptkb:Batch_Normalization gptkb:machine_learning gptkb:Dropout Backpropagation Stochastic Gradient Descent Supervised Learning Unsupervised Learning Large Datasets large datasets Gradient Descent time series data text data Labeled Data audio data sequential data |
| gptkbp:is_used_for |
Anomaly Detection
Dimensionality Reduction Medical Diagnosis Recommendation Systems Time Series Prediction Feature Extraction Community Detection Financial Forecasting Graph Classification Graph Embedding Graph Generation |
| gptkbp:is_used_in |
gptkb:Autonomous_Vehicles
gptkb:Computer_Vision gptkb:Natural_Language_Processing gptkb:Home_Pod gptkb:Apple_TV gptkb:Apple_Watch gptkb:speeches gptkb:machine_learning gptkb:Deep_Learning gptkb:robotics Data Analysis Recommendation Systems language translation Financial Forecasting financial forecasting Game Playing Healthcare Diagnostics chatbots anomaly detection |
| gptkbp:key_feature |
No Fully Connected Layers
|
| gptkbp:operates |
low power consumption
|
| gptkbp:popularized_by |
gptkb:2012
|
| gptkbp:provides |
High Performance Computing
hardware acceleration |
| gptkbp:published_in |
gptkb:2015
|
| gptkbp:requires |
Computational Power
High Computational Power Large Datasets GPU Acceleration backpropagation through time |
| gptkbp:supports |
gptkb:Core_ML
gptkb:machine_learning gptkb:Create_ML real-time processing |
| gptkbp:training |
gptkb:Adam_Optimizer
gptkb:Adam_optimizer Backpropagation Stochastic Gradient Descent stochastic gradient descent RMSprop |
| gptkbp:trends |
gptkb:sustainable_development
Automation Personalization Smart Technologies Human-Machine Collaboration |
| gptkbp:used_for |
gptkb:Pattern_Recognition
gptkb:machine_learning Image Classification Image Processing Image Recognition Object Detection Image Segmentation natural language processing time series analysis Data Classification sequence prediction |
| gptkbp:used_in |
gptkb:art
gptkb:Tensor_Flow gptkb:Generative_Adversarial_Networks Image Segmentation Super Resolution Mac models i Pad models i Phone models |
| gptkbp:uses |
Convolutional Layers
|
| gptkbp:utilizes |
gptkb:neural_networks
|
| gptkbp:bfsParent |
gptkb:Yoshua_Bengio
gptkb:Jürgen_Schmidhuber gptkb:Marvin_Minsky gptkb:Cognitive_Computing gptkb:machine_learning gptkb:Artificial_Neural_Networks gptkb:Geoffrey_R._Hinton gptkb:Yann_Le_Cun |
| gptkbp:bfsLayer |
3
|