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...nguage. Statistical methods, such as [[Hidden Markov Models (HMMs)]] and [[Conditional Random Fields (CRFs)]], have been successfully applied to various NLU tasks ...NLU in recent years. [[Neural Networks]], particularly [[Recurrent Neural Networks (RNNs)]] and [[Transformers]], have demonstrated remarkable success in vari

...ween different classes or categories. These models focus on estimating the conditional probability of a class label given a set of input features, denoted as P(Y| * [[Neural Networks]]: A class of models that consists of interconnected layers of artificial n

Contextual outliers, or conditional outliers, are data points that deviate significantly from their expected be ...hms, such as [[Support Vector Machines]] (SVM), decision trees, and neural networks, are used to build a model that can classify new data points as either norm

...lity to outperform other generative models, such as Generative Adversarial Networks (GANs). <ref name="”1”">O'Connor, R (2022). Introduction to Diffusion M ...for the diffusion process and pass through a UNet, a type of convolutional neural network, that predicts the noise in an image. The noise is then reconstruct

| '''[[ACE]]''' || || [[Alternating conditional expectation algorithm]] | '''[[ANN]]''' || || [[Artificial neural network]]