July 18, 2019
PyTorch Adds New Ecosystem Projects for Encrypted AI and Quantum Computing, Expands PyTorch Hub
The PyTorch ecosystem includes projects, tools, models and libraries from a broad community of researchers in academia and industry, application developers, and ML engineers. The goal of this ecosystem is to support, accelerate, and aid in your exploration with PyTorch and help you push the state of the art, no matter what field you are exploring. Similarly, we are expanding the recently launched PyTorch Hub to further help you discover and reproduce the latest research.
June 10, 2019
Towards Reproducible Research with PyTorch Hub
Reproducibility is an essential requirement for many fields of research including those based on machine learning techniques. However, many machine learning publications are either not reproducible or are difficult to reproduce. With the continued growth in the number of research publications, including tens of thousands of papers now hosted on arXiv and submissions to conferences at an all time high, research reproducibility is more important than ever. While many of these publications are a...
May 22, 2019
torchvision 0.3: segmentation, detection models, new datasets and more..
PyTorch domain libraries like torchvision provide convenient access to common datasets and models that can be used to quickly create a state-of-the-art baseline. Moreover, they also provide common abstractions to reduce boilerplate code that users might have to otherwise repeatedly write. The torchvision 0.3 release brings several new features including models for semantic segmentation, object detection, instance segmentation, and person keypoint detection, as well as custom C++ / CUDA ops sp...
