The AI Monthly Top 3  Papers of October 2021 by@whatsai

The AI Monthly Top 3  Papers of October 2021

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Louis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

Here are the 3 most interesting research papers of the month, in case you missed any of them. It is a curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Enjoy the read, and let me know if I missed any important papers in the comments, or by contacting me directly on LinkedIn!

Paper #1:

Skillful Precipitation Nowcasting using Deep Generative Models of Radar [1]

50+ expert meteorologists assessed DeepMind's new model beating current nowcasting methods in 89% of situations for its accuracy and usefulness!

Read more and link to the code: https://hackernoon.com/ai-endorsed-by-expert-meteorologists-deepminds-weather-forecast-model

Paper #2:

The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks [2]

This AI takes a poorly calibrated audio clip, for example, a movie scene with the music way too loud and actors speaking quietly, and can simply turn up the speech and lower the music!

Read more and link to the code: https://hackernoon.com/this-ai-can-separate-speech-music-and-sound-effects-from-movie-soundtracks

Paper #3:

ADOP: Approximate Differentiable One-Pixel Point Rendering [3]

An AI that takes images as inputs to generate smooth and high quality videos!

Read more and link to the code: https://hackernoon.com/this-ai-creates-videos-from-a-couple-of-images

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If you’d like to read more research papers as well, I recommend you read my article where I share my best tips for finding and reading more research papers.

References

[1] Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S. and Prudden, R., 2021. Skillful Precipitation Nowcasting using Deep Generative Models of Radar, https://www.nature.com/articles/s41586-021-03854-z.

[2] Petermann, D., Wichern, G., Wang, Z., & Roux, J.L. (2021). The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks. https://arxiv.org/pdf/2110.09958.pdf.

[3] Rückert, D., Franke, L. and Stamminger, M., 2021. ADOP: Approximate Differentiable One-Pixel Point Rendering. https://arxiv.org/pdf/2110.06635.pdf.

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