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

by Louis BouchardNovember 2nd, 2021
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The list 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! 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.

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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 !

Paper #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: //gzht888.com/ai-endorsed-by-expert-meteorologists-deepminds-weather-forecast-model

Paper #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: //gzht888.com/this-ai-can-separate-speech-music-and-sound-effects-from-movie-soundtracks

Paper #3:

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

Read more and link to the code: //gzht888.com/this-ai-creates-videos-from-a-couple-of-images

If you like my work and want to stay up-to-date with AI, you should definitely follow me on my other social media accounts (, ) and subscribe to my weekly AI !

If you’d like to read more research papers as well, I recommend you read 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, .

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

[3] Rückert, D., Franke, L. and Stamminger, M., 2021. ADOP: Approximate Differentiable One-Pixel Point Rendering. .

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