google-deepmind/deepmind-research

Features

• Research Reproductions - Provides a collection of implementations designed to reproduce the results and logic of published machine learning research.
• Machine Learning Implementations - Provides a collection of code-based reference implementations of core machine learning algorithms from research papers.
• Neural Network Implementations - Implements specific neural network architectures and algorithms described in academic research papers.
• Paper-to-Code Implementations - Translates academic research papers into functional code to test the metrics and claims of AI publications.
• Research Repositories - Serves as a public archive of experimental code, datasets, and methodologies for advancing AI research.
• Reproducible Research Publishing - Provides the open-source code and datasets necessary to reproduce findings and verify scientific claims from published research.
• Benchmark Validations - Includes standardized implementations to validate and compare model performance against established research baselines.
• JAX Implementations - Ships high-performance neural network implementations using JAX for scalable scientific computing.
• PyTorch Implementations - Ships deep learning implementations utilizing PyTorch for neural network architecture and experimentation.

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