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A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
Recently, there has been a trend to build very large deep generative models. [8] For example, GPT-3, and its precursor GPT-2, [11] are auto-regressive neural language models that contain billions of parameters, BigGAN [12] and VQ-VAE [13] which are used for image generation that can have hundreds of millions of parameters, and Jukebox is a very ...
Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Google (GOOG, GOOGL) on Wednesday debuted its new Gemini generative AI model.The platform serves as Google’s answer to Microsoft-backed OpenAI’s GPT-4, and according to DeepMind CEO Demis ...
The AIs learn a generative model (hence their name) that enables them to predict patterns in various kinds of data or signal. What generative there means is that they learn enough about the deep ...
To enable handling long data sequences, Mamba incorporates the Structured State Space sequence model (S4). [2] S4 can effectively and efficiently model long dependencies by combining continuous-time, recurrent, and convolutional models. These enable it to handle irregularly sampled data, unbounded context, and remain computationally efficient ...