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In 1999, Judi Romijn compared two model checkers (CADP and SPIN) on the HAVi interoperability audio-video protocol for consumer electronics. [3] In 2003, Yifei Dong, Xiaoqun Du, Gerard J. Holzmann, and Scott A. Smolka published a comparison of four model checkers (namely: Cospan, Murphi, SPIN, and XMC) on a communication protocol, the GNU i ...
The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().
In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
In addition to model-checking, SPIN can also operate as a simulator, following one possible execution path through the system and presenting the resulting execution trace to the user. Unlike many model-checkers, SPIN does not actually perform model-checking itself, but instead generates C sources for a problem-specific model checker.
Conda is an open-source, [2] cross-platform, [3] language-agnostic package manager and environment management system. It was originally developed to solve package management challenges faced by Python data scientists, and today is a popular package manager for Python and R.
Principles of Model Checking is a textbook on model checking, an area of computer science that automates the problem of determining if a machine meets specification requirements. It was written by Christel Baier and Joost-Pieter Katoen , and published in 2008 by MIT Press .
PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.