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- Neural Network Pruning Description: Neural network pruning is a technique used to reduce the size of a neural network by removing weights. This process is carried out(...) Read more
- Noise Masking Description: Noise masking is a technique used in computer vision to mitigate the impact of noise in image processing. This noise can be caused(...) Read more
- Neural Data Augmentation Description: Neural Data Augmentation is a technique that uses neural networks to generate additional data that complements an existing dataset.(...) Read more
- Normalizing Flow Description: The normalizing flow is a generative model that transforms a simple distribution into a more complex one through a series of(...) Read more
- Neural Process Description: The Neural Process is a generative model that combines neural networks with stochastic processes to learn distributions over(...) Read more
- 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
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