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Gradual Self-Training

What is Gradual Self-Training? Gradual self-training is a machine learning method for semi-supervised domain adaptation. This technique involves adapting an initial classifier, which has been trained on a source domain, in such a way that it can predict on unlabeled data sets that experience a shift gradually towards a target domain. This approach has numerous potential applications in domains like self-driving cars and brain-machine interfaces, where machine learning models must adapt to chang

Grammatical evolution and Q-learning

Grammatical evolution and Q-learning are two powerful techniques in the field of artificial intelligence. Grammatical evolution is a method used to evolve a grammar for building an intelligent agent while Q-learning is used in fitness evaluation to allow the agent to learn from its mistakes and improve its performance. What is Grammatical Evolution? Grammatical evolution is a search algorithm used to generate computer programs using a set of rules, also known as a grammar. The input to the al

Graph Attention Network v2

Graph Attention Networks (GATs) are a type of neural network used for processing graph data, which is data with complex relationships. GATs use attention mechanisms to focus on the most relevant nodes in a graph when making predictions. However, the standard GAT layer has a "static attention problem" where the ranking of attended nodes is unconditioned on the query node. This is where GATv2 comes in. What is GATv2? GATv2 is an operator introduced in the "How Attentive are Graph Attention Netw

Graph Attention Network

Graph Attention Network (GAT): A Revolutionary Neural Network Architecture Artificial Intelligence (AI) works on a simple mechanism of feeding data into a neural network-based system, following steps, patterns, and historical data to give an output. However, traditional machine learning (ML) models operate on data points that are not usually interlinked. At the same time, real-world data presents a much more complex problem in the form of networks with different relations between data points. G

Graph Contrastive Coding

Graph Contrastive Coding (GCC) is a self-supervised pre-training framework for graph neural networks. Its goal is to capture the universal network topological properties across multiple networks. GCC is designed to learn intrinsic and transferable structural representations of graphs. What is GCC? Graph Contrastive Coding is a self-supervised method for capturing the topological properties of graphs. GCC uses a pre-training task called subgraph instance discrimination, which is designed to wo

Graph Convolutional Network

Overview of Graph Convolutional Network (GCN) A Graph Convolutional Network, or GCN, is a method for semi-supervised learning on graph-structured data. It is based on a variant of convolutional neural networks that work directly on graphs. This method is efficient and has been shown to be effective in encoding both local graph structure and node features through hidden layer representations. How does GCN work? GCN operates on graph-structured data where nodes are connected by edges. This typ

Graph Convolutional Networks for Fake News Detection

Overview of GCNFN Social media has become a major news source for millions of people around the world due to its low cost, easy accessibility, and rapid dissemination. However, this comes at the cost of dubious trustworthiness and a significant risk of exposure to fake news, intentionally written to mislead the readers. Detecting fake news is a challenge that is difficult to overcome using existing content-based analysis approaches. One of the main reasons for this is that often the interpretat

Graph Convolutional Networks

What are Graph Convolutional Networks? Graph Convolutional Networks, or GCN, are a type of neural network used for semi-supervised learning on graph-structured data. They are designed to operate directly on graphs, which makes them a valuable tool for tasks that involve data in graph format. GCN is based on an efficient variant of convolutional neural networks (CNNs) that are commonly used for image recognition tasks. The main difference between the two is that while CNNs operate on regular gr

Graph Echo State Network

The Graph Echo State Network, or GraphESN, is a type of neural network that is designed to work with graphs. It is an extension of the Echo State Network (ESN), which is a type of recurrent neural network. What is a Graph? First, let’s make sure we all understand what we mean by a graph. In mathematics, a graph is a way of representing relationships between objects. It is made up of vertices (also called nodes) and edges. A vertex can represent anything, from a person to a city to a gene, and

Graph Finite-State Automaton

GFSA or Graph Finite-State Automaton is a layer that can be used for learning graph structure. This layer is differentiable, which means it can be trained end-to-end to add derived relationships or edges to arbitrary graph-structured data. GFSA works by adding a new edge type, expressed as a weighted adjacency matrix, to a base graph. This layer has been designed to be used in machine learning applications. What is GFSA? If you are familiar with machine learning and graph structure, you may h

Graph Isomorphism Network

Gin has become the latest trend in the world of data science and artificial intelligence. It is an acronym for Graph Isomorphism Network, and it has been generating a lot of buzz in the scientific community. This algorithm has been hailed as being one of the most discriminative GNNs available, as it utilizes a process known as the WL test. What is Gin? Gin, which stands for Graph Isomorphism Network, is a new machine learning algorithm designed for data scientists, artificial intelligence sys

Graph Network-based Simulators

GNS, or Graph Network-Based Simulators, is an innovative approach to modeling complex physical systems. By using a graph neural network to represent the state of a system, GNS allows for accurate computation of system dynamics through learned message-passing. What is GNS? Graph Network-Based Simulators, or GNS, is a type of graph neural network that models the behavior of physical systems by representing particles as nodes in a graph. Through learned message-passing, GNS calculates the dynami

Graph Neural Networks with Continual Learning

Introduction to GNNCL: Solving the Problem of Fake News on Social Media In today's world, social media has become a ubiquitous tool for sharing news and staying connected with friends and family. However, the widespread usage of social media has also led to the proliferation of fake news, which can have devastating consequences on our society. The ability to differentiate between fake and real news is critical to maintaining public trust in our institutions and preserving the integrity of our d

Graph Path Feature Learning

Understanding GPFL Graph Path Feature Learning (GPFL) is a powerful tool used to extract rules from knowledge graphs. These extracted rules are used to improve our understanding of complex concepts and relationships between different elements in these graphs. The Importance of Extracting Rules from Knowledge Graphs Knowledge graphs are large collections of data that organize information in a way that presents the relationships between different elements. These graphs are often used to make s

Graph sampling based inductive learning method

Introduction to GraphSAINT GraphSAINT is a powerful new tool that helps train large scale graph neural networks (GNNs) more efficiently. GNNs are a type of artificial intelligence that can learn from data that exists in the form of graphs. Graphs are used to represent relationships between different objects. For example, a social network could be represented as a graph, where each person is a node in the graph and relationships between people (friends, family members, colleagues, etc.) are edg

Graph Self-Attention

Graph Self-Attention: An Overview Graph Self-Attention, or GSA for short, is a self-attention module used in BP-Transformer architecture. It is based on the graph attentional layer, which helps update the node's representation based on the neighboring nodes. GSA is a technique used in Natural Language Processing, which has gained significant popularity from the year 2017. What is Graph Self-Attention? Graph Self-Attention is a technique used in Natural Language Processing or NLP, where we ai

Graph Transformer

Graph Transformer: A Generalization of Transformer Neural Network Architectures for Arbitrary Graphs The Graph Transformer is a method proposed as a generalization of Transformer Neural Network architectures, designed for arbitrary graphs. This architecture is an enhanced version of the original Transformer and comes with several highlights, making it unique in its approach. Attention Mechanism The attention mechanism is a crucial part of the Graph Transformer architecture. Unlike the origin

Graphic Mutual Information

What is GMI? GMI, also known as Graphic Mutual Information, is a measurement method used to determine the correlation between input graphs and high-level hidden representations. Different from the conventional mutual information computations that take place in vector space, GMI extends the calculation to the graph domain. Measuring mutual information from two aspects of node features and topological structure is essential in the graph domain, and GMI makes that possible. Benefits GMI provide

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Dependency Parsers Deraining Models Detection Assignment Rules Dialog Adaptation Dialog System Evaluation Dialogue State Trackers Dimensionality Reduction Discriminators Distillation Distributed Communication Distributed Methods Distributed Reinforcement Learning Distribution Approximation Distributions Document Embeddings Document Summary Evaluation Document Understanding Models Domain Adaptation Downsampling E-signing Efficient Planning Eligibility Traces Ensembling Entity Recognition Models Entity Retrieval Models Environment Design Methods Exaggeration Detection Models Expense Trackers Explainable CNNs Exploration Strategies Face Privacy Face Recognition Models Face Restoration Models Face-to-Face Translation Factorization Machines Feature Extractors Feature Matching Feature Pyramid Blocks Feature Upsampling Feedforward Networks Few-Shot Image-to-Image Translation Fine-Tuning Font Generation Models Fourier-related Transforms Free AI Tools Free Subscription Trackers Gated Linear 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Detection Models Mean Shift Clustering Medical Medical Image Models Medical waveform analysis Mesh-Based Simulation Models Meshing Meta-Learning Algorithms Methodology Miscellaneous Miscellaneous Components Mixture-of-Experts Model Compression Model Parallel Methods Momentum Rules Monocular Depth Estimation Models Motion Control Motion Prediction Models Multi-Modal Methods Multi-Object Tracking Models Multi-Scale Training Music Music source separation Music Transcription Natural Language Processing Natural Language Processing Courses Negative Sampling Network Shrinking Neural Architecture Search Neural Networks Neural Networks Courses Neural Search No Code AI No Code AI App Builders No Code Courses No Code Tools Non-Parametric Classification Non-Parametric Regression Normalization Numpy Courses Object Detection Models Object Detection Modules OCR Models Off-Policy TD Control Offline Reinforcement Learning Methods On-Policy TD Control One-Stage Object Detection Models Open-Domain 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Inpainting Models Video Instance Segmentation Models Video Interpolation Models Video Model Blocks Video Object Segmentation Models Video Panoptic Segmentation Models Video Recognition Models Video Super-Resolution Models Video-Text Retrieval Models Vision and Language Pre-Trained Models Vision Transformers VQA Models Webpage Object Detection Pipeline Website Monitoring Whitening Word Embeddings Working Memory Models