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- Non-linear Generative Model Description: A non-linear generative model is a statistical approach that allows capturing complex and non-trivial relationships in data through(...) Read more
- Neural Network Generative Model Description: A generative neural network model is a type of machine learning architecture used to learn the distribution of a dataset and(...) Read more
- Noise-Contrastive Estimation Description: Noise Contrastive Estimation is a technique used in generative models that focuses on training models by contrasting observed data(...) Read more
- Non-parametric Generative Model Description: A non-parametric generative model is an approach in the field of machine learning and statistics that does not assume a fixed(...) Read more
- Neural Autoregressive Distribution Description: The Neural Autoregressive Distribution is a generative model that employs neural networks to capture and model complex data(...) Read more
- Non-stationary Process Description: The non-stationary process refers to a generative model that considers changes in the underlying data distribution over time.(...) Read more
- Nested Sampling Description: Nested sampling is a statistical method used to estimate evidence in generative models, particularly in Bayesian contexts. This(...) Read more
- Neural Bayesian Inference Description: Neural Bayesian Inference is a generative modeling approach that integrates neural networks with Bayesian inference techniques,(...) Read more
- N-gram Model Description: The N-gram model is a generative approach used in natural language processing (NLP) that is based on the probability of occurrence(...) Read more
- Neural Generative Adversarial Network Description: The Generative Adversarial Network (GAN) is a type of generative model that uses an adversarial training approach to generate new(...) Read more
- Non-homogeneous Markov Model **Description:** A non-homogeneous Markov model is a type of generative model that allows transitions between states to vary over time, unlike(...) Read more
- Nash Bargaining Solution Description: The Nash Bargaining Solution is a fundamental concept in game theory, specifically in the realm of cooperative games. This solution(...) Read more
- Natural Gradient Description: Natural gradient is an optimization technique used in the training of generative models, particularly in the context of machine(...) Read more
- Negative Log-Likelihood Description: Negative Log-Likelihood is a loss function used in generative models to evaluate how well the model fits the observed data. This(...) Read more
- Neural Representation Learning Description: Neural representation learning focuses on learning representations from multimodal data using deep learning techniques. This(...) Read more
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