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 with a , , and (if applicable). Enjoy the read, and let me know if I missed any important papers in the comments, or by contacting me directly on ! latest breakthroughs in AI and Data Science by release date clear video explanation link to a more in-depth article code 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 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 ! LinkedIn Twitter newsletter 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. my article 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
