How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Introduction
Neural networks are a type of machine learning algorithm that are modeled after the structure and function of the human brain. They are used to solve a wide range of problems, from image recognition to natural language processing. One of the key features of neural networks is their ability to extrapolate, or make predictions based on patterns in data. In this article, we will explore how neural networks extrapolate, from the basic feedforward network to the more complex graph neural networks.
Feedforward Neural Networks
Feedforward neural networks are the simplest type of neural network. They consist of an input layer, one or more hidden layers, and an output layer. The input layer receives data, which is then passed through the hidden layers to the output layer. Each neuron in the hidden layers performs a weighted sum of its inputs, applies an activation function, and passes the result to the next layer. The output layer produces the final prediction based on the input data.
Feedforward neural networks are good at extrapolating patterns in data, but they have limitations. They can only handle structured data, such as numerical values, and they are not well-suited for handling unstructured data, such as images or text.
Recurrent Neural Networks
Recurrent neural networks (RNNs) are a type of neural network that can handle sequential data, such as time series or natural language. They have a feedback loop that allows information to be passed from one time step to the next. This makes them well-suited for tasks such as speech recognition or language translation.
RNNs can also extrapolate patterns in data, but they have limitations. They can suffer from the vanishing gradient problem, which makes it difficult for them to learn long-term dependencies. They also have difficulty handling variable-length sequences, which can be a problem for tasks such as natural language processing.
Convolutional Neural Networks
Convolutional neural networks (CNNs) are a type of neural network that are designed for image recognition. They use convolutional layers to extract features from images, and pooling layers to reduce the dimensionality of the feature maps. This makes them well-suited for tasks such as object recognition or facial recognition.
CNNs can also extrapolate patterns in data, but they have limitations. They are not well-suited for handling unstructured data other than images, such as text or audio. They also have difficulty handling variable-sized inputs, which can be a problem for tasks such as object detection.
Graph Neural Networks
Graph neural networks (GNNs) are a type of neural network that are designed for handling graph-structured data, such as social networks or chemical compounds. They use message passing to propagate information between nodes in the graph, and graph convolutional layers to extract features from the graph structure.
GNNs are well-suited for extrapolating patterns in graph-structured data, but they have limitations. They can be computationally expensive, especially for large graphs. They also have difficulty handling graphs with variable structure, such as those that change over time.
Conclusion
Neural networks are a powerful tool for extrapolating patterns in data. From the basic feedforward network to the more complex graph neural networks, there are a variety of neural network architectures that can be used to solve different types of problems. By understanding the strengths and limitations of each type of neural network, we can choose the best architecture for our specific task.
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1.How Neural Networks Extrapolate: From Feedforward to Graph …
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- Source : https://arxiv.org/abs/2009.11848
2.[PDF] FROM FEEDFORWARD TO GRAPH NEURAL NETWORKS
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- Descriptions: We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution.
- More : We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution.
- Source : https://openreview.net/pdf%3Fid%3DUH-cmocLJC
3.How Neural Networks Extrapolate: From … – CrossMind.ai
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4.How Neural Networks Extrapolate: From Feedforward to … – Papertalk
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- Descriptions: Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks.
- More : Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks.
- Source : https://papertalk.org/papertalks/29183
5.How Neural Networks Extrapolate: From … – ResearchGate
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6.[PDF] Graph Neural Networks: Generalization and Extrapolation – People
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- Descriptions: How do neural networks extrapolate? Depends on inductive bias of gradient descent training and neural network. Parameter trajectory θ(t) …
- More : How do neural networks extrapolate? Depends on inductive bias of gradient descent training and neural network. Parameter trajectory θ(t) …
- Source : https://people.csail.mit.edu/keyulux/pdf/extrapolate.pdf
7.jinglingli/nn-extrapolate – GitHub
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- Descriptions: How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. This repository is the PyTorch implementation of the experiments in the …
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8.[R] How Neural Networks Extrapolate: From Feedforward to Graph …
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9.How Artificial Neural Networks Reimagine Data Extrapolation
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- Source : https://christophegaron.com/articles/mind/how-artificial-neural-networks-reimagine-data-extrapolation-from-feedforward-to-graph-neural-networks/
10.How Neural Networks Extrapolate: From Feedforward to Graph …
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- Descriptions: We study how neural networks trained by gradient descent extrapolate, i.e., … Graph Neural Networks (GNNs), a structured network with MLP modules, …
- More : We study how neural networks trained by gradient descent extrapolate, i.e., … Graph Neural Networks (GNNs), a structured network with MLP modules, …
- Source : http://paperreading.club/page%3Fid%3D56924