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Graph-tool a Python library for graph manipulation and visualization. OmniGraffle version 5 and later uses the Graphviz engine, with a limited set of commands, for automatically laying out graphs. [9] Org-mode can work with DOT source code blocks. [10] PlantUML uses Graphviz to generate UML diagrams from text descriptions.
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
A graphical abstract (or visual abstract [1]) is a graphical or visual equivalent of a written abstract. [2] [3] Graphical abstracts are a single image and are designed to help the reader to quickly gain an overview on a scholarly paper, research article, thesis or review: and to quickly ascertain the purpose and results of a given research, as well as the salient details of authors and journal.
GPT-2 can generate thematically-appropriate text for a range of scenarios, even surreal ones like a CNN article about Donald Trump giving a speech praising the anime character Asuka Langley Soryu. Here, the tendency to generate nonsensical and repetitive text with increasing output length (even in the full 1.5B model) can be seen; in the second ...
Generative AI systems such as MusicLM [72] and MusicGen [73] can also be trained on the audio waveforms of recorded music along with text annotations, in order to generate new musical samples based on text descriptions such as a calming violin melody backed by a distorted guitar riff.
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License.It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".
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.
Information visualization, on the other hand, deals with multiple, large-scale and complicated datasets which contain quantitative (numerical) data as well as qualitative (non-numerical, i.e. verbal or graphical) and primarily abstract information and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their ...