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- Sequence to Sequence Description: Sequence to sequence is a model architecture that transforms an input sequence into an output sequence, commonly used in natural(...) Read more
- Spatial Pooling Description: Spatial pooling, also known as 'pooling', is a fundamental operation in convolutional neural networks (CNNs) used to reduce the(...) Read more
- Stacked Autoencoders Description: Stacked autoencoders are a type of neural network that consists of multiple layers of autoencoders, where each layer is trained to(...) Read more
- Semantic Memory Description: Semantic memory is a type of memory that focuses on the storage and retrieval of facts and concepts, as opposed to episodic memory,(...) Read more
- Spatial Transformation Description: Spatial transformation is a technique used to manipulate the spatial dimensions of data, often applied in image processing tasks.(...) Read more
- Sample Efficiency Description: Sample efficiency in neural networks refers to a model's ability to achieve good performance with a limited amount of training(...) Read more
- Sparsity-Inducing Regularization Description: Sparsity-inducing regularization is a technique used in the field of machine learning and neural networks to promote sparsity in(...) Read more
- Specificity Description: Specificity in the context of supervised learning refers to a model's ability to correctly identify negative instances within a(...) Read more
- Supervised Regression Description: Supervised regression is an approach within supervised learning that focuses on predicting continuous values. In this type of(...) Read more
- Subspace Clustering Description: Subspace clustering is a clustering method that focuses on identifying clusters within subspaces of high-dimensional data. Unlike(...) Read more
- Stochastic Neighbor Embedding Description: Stochastic Neighbor Embedding (t-SNE) is a non-linear dimensionality reduction technique that allows for the visualization of(...) Read more
- Statistical Clustering Description: Statistical clustering is an unsupervised learning method used to group a dataset into clusters based on similar characteristics.(...) Read more
- Semantic Clustering Description: Semantic clustering is a fundamental process in the field of natural language processing (NLP) and large language models. It refers(...) Read more
- Spectral Embedding Description: Spectral embedding is a dimensionality reduction method based on the spectrum of the Laplacian matrix of a graph. This approach(...) Read more
- Self-Organizing Feature Map Description: The Self-Organizing Map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce a(...) Read more
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