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README.md

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+PyKale is a library in the [PyTorch ecosystem](https://pytorch.org/ecosystem/) aiming to make machine learning more accessible to interdisciplinary research by bridging gaps between data, software, and end users. Both machine learning experts and end users can do better research with our accessible, scalable, and sustainable design, guided by green machine learning principles. PyKale has a unified *pipeline-based* API and focuses on [multimodal learning](https://en.wikipedia.org/wiki/Multimodal_learning) and [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning) for graphs, images, and videos at the moment, with supporting models on [deep learning](https://en.wikipedia.org/wiki/Deep_learning) and [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction).
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+PyKale enforces *standardization* and *minimalism*, via green machine learning concepts of *reducing* repetitions and redundancy, *reusing* existing resources, and *recycling* learning models across areas. PyKale will enable and accelerate *interdisciplinary*, *knowledge-aware* machine learning research for graphs, images, and videos in applications including bioinformatics, graph analysis, image/video recognition, and medical imaging, with an overarching theme of leveraging knowledge from multiple sources for accurate and *interpretable* prediction.
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+See our [arXiv preprint](https://arxiv.org/abs/2106.09756) and four short introductory videos on YouTube: [Why build PyKale?](https://youtu.be/nybYgw-T2bM) [How was PyKale built?](https://youtu.be/jaIbkjkQvYs) [What's in PyKale?](https://youtu.be/I3vifU2rcc0) and [a 5-min summary](https://youtu.be/Snou2gg7pek).