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Sentence-BERT (SBERT) is a modification of the BERT network designed to generate semantically meaningful sentence embeddings. It allows efficient and accurate sentence similarity comparisons and clustering in natural language processing tasks.
Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of data by transforming it into a new set of variables called principal components. These components capture the maximum variance within the data, enabling easier visualization, interpretation, and noise reduction.
PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab. It is widely used for applications such as computer vision and natural language processing, known for its dynamic computational graph and ease of use.
WinoGrande is a large-scale dataset designed for evaluating commonsense reasoning in natural language processing. It extends the Winograd Schema Challenge by providing thousands of carefully constructed sentence pairs that test an AI’s ability to resolve ambiguous pronouns.
The Set Transformer is a neural network architecture designed for processing sets of data, notable for its ability to handle variable-sized inputs effectively.
A multilayer perceptron (MLP) is a class of feedforward artificial neural network with multiple layers of nodes that processes data in a manner inspired by the human brain.
Third-person imitation learning is a machine learning technique where an autonomous agent learns to perform tasks by observing demonstrations from a third-person perspective. It enables learning from videos or observations where the demonstrator’s viewpoint differs from the learner’s, facilitating broader applications in robotics and AI.
The Naive Bayes classifier is a probabilistic machine learning algorithm used for classification tasks based on Bayes’ theorem, assuming independence among predictors.
Stochastic weight averaging–Gaussian (SWAG) is a technique in machine learning that improves model generalization and uncertainty estimation by approximating the posterior distribution of neural network weights using Gaussian distributions derived from stochastic weight averaging. It enhances predictive performance and reliability in deep learning models.
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics used to evaluate the quality of summaries and machine-generated text by comparing them to reference texts. It is widely applied in natural language processing tasks, particularly for automated summarization evaluation.