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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.
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
A simple flowchart representing a process for dealing with a non-functioning lamp.. A flowchart is a type of diagram that represents a workflow or process.A flowchart can also be defined as a diagrammatic representation of an algorithm, a step-by-step approach to solving a task.
When using UML, the activity diagram typically takes over the role of the data-flow diagram. A special form of data-flow plan is a site-oriented data-flow plan. Data-flow diagrams can be regarded as inverted Petri nets, because places in such networks correspond to the semantics of data memories. Analogously, the semantics of transitions from ...
Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), [73] Google Photos, [74] and the Adobe Suite (Adobe Firefly). [75] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [76] language model.
Companies will soon focus on customizing AI solutions for specific needs, Cohere's CEO says. AI 2.0 will "help fundamentally transform how businesses operate," he wrote. Major AI companies like ...
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