#Nvidia introduced the Latent Point Diffusion Model (LION), a DDM for 3D shape generation. LION focuses on learning a 3D generative model directly from geometry data without image-based training.
https://bit.ly/3DCuu5q

Google Research and researchers at Tel Aviv University have created a method that enables editing generated images by only modifying the textual prompt.
https://bit.ly/3THUTUG

The Data Phoenix Events team invites you all to our upcoming «The A-Z of Data» charity webinar that’s going to take place on November 9, 2022 at 16.00 CET.

- Topic: «Learning Through Machine Learning: How We Built a Recommendation System from Scratch».
- Speaker: Dora Petrella, Senior Data Scientist at METRO.digital
- Language: English
- Participation: free (but you’ll be required to register)
- Karma perk: donate to our charity initiative

https://bit.ly/3DfNIfF

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DataPhoenix presented the new issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe👇🏻
https://bit.ly/3FnlsKy

Sequoia Capital has unveiled the Gen AI market map - The Generative AI Application Landscape, which brings together the best projects. Among them are Copy.ai, Contenda, Hypotenuse, Get Writer, GitHub, Stability AI, Craiyon, and many others!
https://bit.ly/3TXPlWn

Meta Fundamental AI Research (FAIR) team on audio hypercompression shows how AI can be used to ensure that audio messages don't glitch or slow down when the Internet connection is poor.
https://bit.ly/3Dqsrju

AutoAvatar is an autoregressive approach for modeling dynamically deforming human bodies directly from raw scans.
https://bit.ly/3gZ3Pqn

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DataPhoenix presented the new issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe👇🏻
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YOWO-Plus: An Incremental Improvement
Developers make updates to the YOWO design to make it better. For the network structure, they use official YOWO, including 3D-ResNext-101, and the best pre-trained weight of the re-implemented YOLOv2, which is better than the official YOLOv2.
https://bit.ly/3U5jIdB

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CALA uses DALL-E to generate designs for clothing, accessories, footwear, and lifestyle products
https://bit.ly/3tcUxtW

The new Musika music generation system can be trained on hundreds of hours of music using a single consumer GPU, and allows much faster generation of arbitrary length music on a consumer CPU.
https://bit.ly/3TikCSP

The eDiff-I diffusion model for image synthesis from text is a next-generation tool for generative AI content creation that offers unprecedented text-to-image fusion, instant style transfer, and intuitive word-drawing capabilities.
https://bit.ly/3TlQi9U

📢 We are live and starting our charity webinar about recommendation systems: "Learning Through Machine Learning: How We Built a Recommendation System from Scratch”. Join us: https://bit.ly/3EeQrro

DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2 million images generated by Stable Diffusion using prompts and hyperparameters specified by real users.
https://bit.ly/3TuPKOS

​​⚡️Hello friends!
DataPhoenix presented the new issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe👇🏻
https://bit.ly/3UMvwkP

Data Phoenix team invites you all to our upcoming "The A-Z of Data" charity webinar that’s going to take place on November 16, 2022 at 16.00 CET.
Topic: “Natural Language Processing (NLP) and Machine Learning in Healthcare”.
Speaker: Dr. Hemachandran Kannan, Zita Zoltay Paprika Professor of Decision Sciences & Business Economics and Course5i Chair Professor of Business Analytics and Machine Learning at Woxsen University
Language: English
Participation: free (but you’ll be required to register)
Karma perk: donate to our charity initiative
https://bit.ly/3X5toqa

📢 We are live and starting our charity webinar: "Natural Language Processing and Machine Learning in Healthcare”.
Join us: https://bit.ly/3g6VsZZ

Scaling Instruction-Finetuned Language Models
In this paper, authors explore instruction finetuning with a particular focus on scaling the number of tasks, scaling the model size, and finetuning on chain-of-thought data.
https://bit.ly/3hUF8vN

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DataPhoenix presented the new issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe👇🏻
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ULNeFs solve multi-object interaction using implicit representations of the objects (e.g., multiple garments).
https://bit.ly/3TRDVmj