Learn more about the submission categories:

  • PyTorch Responsible AI Development Tools & Libraries: Build an AI development that improves and integrates with work and personal lives. Read more.
  • Web and Mobile Applications powered by PyTorch: Build an application with the web, mobile interface, and/or embedded device powered by PyTorch. Read more.
  • PyTorch Developer Tools & Libraries: Build a creative, useful, and well-implemented tool or library for improving the productivity and efficiency of PyTorch researchers and developers. Read more.

1 - PyTorch Responsible AI Development Tools & Libraries

AI developments are improving and integrating with our work and personal lives. It is essential that we, as PyTorch researchers and developers, put our efforts into building tools, libraries, and web/mobile applications that help develop AI models and applications responsibly. These tools, libraries, apps need to support a researcher or developer to factor in fairness, security, and privacy throughout the entire machine learning development process such as data gathering, model training, model validation, inferences, monitoring, and more. 

Some questions, not limited to, that these tools need to help answer:

  • Is my model biased on a specific group of people based on their race, income, sexuality, nationality, or limited body abilities?
  • Do I know my data sets for training and validation are inclusive?
  • Is my model development process secure, so it doesn’t put personal information at risk?
  • Do I understand the social impact of my model inferences?
  • Do I understand how my model makes predictions/ decisions?
  • How robust is my model to different types of attacks/ transformations?

Data sets:

Problem statements:

  • How to detect and mitigate fairness issues in models

  • Explain model mis-classification  and how could we improve/ mitigate the issues of mis-classification?

  • How can we measure models' vulnerability to adversarial attacks and transformations?

Remember, responsible AI is not the same as AI for good. Here are some example projects for this category:

2 - Web and mobile applications powered by PyTorch

Build an application with the web, mobile interface, and/or embedded device powered by PyTorch. The submission must be built on PyTorch (submissions can use PyTorch-based libraries like torchvision, torchtext, fast.ai, etc...) and have a web, mobile interface, or is presented as an embedded device, so the end users can interact with it. To learn more about PyTorch mobile, visit https://pytorch.org/mobile/home.

Here are some example projects for this category:

3 - PyTorch Developer Tools & Libraries

Build a creative, useful and well-implemented tool or library for improving productivity and efficiency of PyTorch researchers and developers. The tool or library must be a machine learning algorithm, model, or an application. It should help with tasks such as debugging, training, model understanding, encryption, deployment or furthering research. The submission must be built on PyTorch (submissions can use PyTorch-based libraries like torchvision, torchtext, fast.ai, etc...)

Here are some example projects for this category: