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Below code was utilized create unform graph vectors based on nodes and edges of a medical graph dictionary with 500 nodes (body parts, cellular structure, diseases, medical treatment, symptoms), hierarchical order (parent, child) and medical relationship edges (treated_with, contains, experiences….)

https://preview.redd.it/23erwhe8kwme1.png?width=1024&format=png&auto=webp&s=ace05837ac807fd58f438e939d3a72fddc6226ee Hello everyone, I’ve been exploring different models and their ability to craft jokes, and I’m curious: which AI, in your opinion, writes the best jokes? submitted by /u/gargolopereyra [link] [comments]

I already created a medical graph dictionary with nodes and edges, generated uniform graph vectors (85%) and combined them with MiniLLM vectors (15%) and utilized successfully in MLM and CLM (preidict next token) training. With only 500 Pubmed data samples

The duck song 🦆🍇🍋 submitted by /u/RedditUserBrasileiro [link] [comments]

I have two things in mind that could help me a lot. I apologise if those are easy to find but I can’t seem to find anything using just google. I mostly find AI-art tools using prompt, which I’m not

submitted by /u/Primordial_Fupa [link] [comments]

Soilless growing systems inside greenhouses, known as controlled environment agriculture, promise to advance the year-round production of high-quality specialty crops, according to an interdisciplinary research team. But to be competitive and sustainable, this advanced farming method will require the development

I just read the Spark-TTS paper, and it introduces a really clever approach to text-to-speech: a single-stream architecture with decoupled speech tokens that represents both content and acoustic features in a unified sequence. The key technical highlights: * Uses “DCC”

submitted by /u/Tiny-Independent273 [link] [comments]

submitted by /u/Nunki08 [link] [comments]