Search Results for "parallel"

...It supports 240+ languages using DeepL, Google, OpenAI, Azure, and Google Gemini models. The interface is available in 18 languages. Features • 3 Translation Modes: Fluent (parallel), Area (overlay), Full Screen (smart detection) • Speaker detection with color-coding • Glint AI custom terminology control • Game-based profile system • Advanced settings with 50+ parameters for fine-tuned control • Share and import custom profiles (.glint) between users • Low CPU/RAM usage, optimized for Windows 10/11 Live Subtitle (Real-Time Voice Translation) Real-time speech-to-text translation for games, movies, and voice chats. ...

This is a software agent based computing program that will enable researchers and other users to tap in computing power of machine available by sharing work load on the fly with zero configuration on network & resources A self organizing agent program that will understand network and its resource. where as the only job left to researcher is to split up jobs in several chunks of programs either parallel or sequential jobs and go issue the job (A visual Modeler or Scripting support need to be yet designed) Software agents will automatically manage the rest or resource management, sharing , cloning of tasks etc. new resources can be added and removed from the system on fly; in layman terms the project will create an agent program that enable sharing & execution of program among all the available resources whether it be desktop, laptop, pda . thereby one can accelerate research to the very extent of resource availability with out bothering about anything... ...

...CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. ...

This project uses massively parallel Graphics Processing Units(GPU) for neural network(Backpropagation) purposes.

A WebCrawler for Natural Language Processing. This WebCrawler searches for monolingual (in a specified language) and bilingual, parallel text.

A .Net Library that lets you make full use of multi-core and multiprocessor computers. It is very efficient, and easily integrated into existing applications. It is modeled after the Parallel FX usage model, and is faster than the PFX too.