#complexdecisionmakingusingneural résultats de recherche

New energy-efficient and probabilistic computing device functions in a more brain-like manner #probabilisticcomputing #complexdecisionmakingusingneural networks #Gaussianfieldeffecttransistor #probabilisticneuralnetworks #penn_state innovationtoronto.com/2019/09/new-en…

“Transform your image into high-end, editorial vintage portraits using AI” Timeless Vintage Realism: The 70s Collection 🎥 How to: 1. Open the Gemini app 2. Upload a selfie 3. Select a style (prompts below) 4. Feel the magic 5. Follow @ShreyaYadav___ for more AI photoshoots…

i’ve tested 99% of AI image generation methods since i started making these videos... 99% of outputs are slop, but me and my team have discovered a few really good frameworks that let’s us create extremely tasteful and high quality images We train our images off of the…

5 Decision frameworks for tough situations

💥DP_SGD fine tune generative model on the original dataset to generate hierarchical data for synthetic Photo albums ▶️ #AI #MachineLearning #GenAI #GenerativeAI research.google/blog/a-picture…

Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices.…

#NEUROLOGY #Ai High-level visual representations in the human brain are aligned with large language models news.ycombinator.com/item?id=451437…

Chaotic Motion Blur Portraits by @emanemaphoto using ChatGPT Image 🌪️

What if bigger models are actually more interpretable? Mixture of Experts (MoE) have become central to scaling LLMs and are used in nearly every frontier model. Yet we still lack a mechanistic understanding of how MoEs represent features differently than dense models. How do…

This was one of the best ML freelance projects I have ever worked on. It combines multiple features vectors into a single large vector embedding for match-making tasks. It contains so many concepts - > YOLO > Grabcut Mask > Color histograms > SIFT ( ORB as alternate ) > Contours…

Say hello to DINOv3 🦖🦖🦖 A major release that raises the bar of self-supervised vision foundation models. With stunning high-resolution dense features, it’s a game-changer for vision tasks! We scaled model size and training data, but here's what makes it special 👇

okay i'm trying to create a better masking process, so that we could detect more line segments per image instead of missing them. in the first image we are able to capture all the line segments nicely, while the second image shows that we are missing the ones further away

This architecture represents how a Convolutional Neural Network (CNN) transforms raw images into intelligent predictions. Through layers of Convolution, ReLU, Pooling, and Fully Connected Networks, it learns to see, understand, and classify — just like the human brain.

Can neural nets fill point cloud holes in screen space effectively?🤔I think they can! The input data lacks 15% of the visible points and is filtered by a 7x7 convnet based on normals and depth only at the moment. It should run in real time. 1/3🧵

I came up with a technique for dynamic token selection in Vision-Language Models. Instead of wasting compute on every part of an image, this method adapts the number of tokens based on the complexity of each region. Here’s an example of how it works: 👇

Enjoy and pick your favorite! 📸🎇 Try yours, link in bio. 🔗 #Photo #Image #Pro #Filter

It's getting harder and harder to tell if an image was generated by artificial intelligence. I've circled some clues here that might mean this is AI.

Most AI image models break the SECOND you ask for clean text or logos. This one is the exception. I’ve been testing a new model called Qwen Edit. And it solves one of the biggest pain points in AI creative… Editing specific parts of an image WHILE maintaining consistency. -…

I tried out neural filters in Photoshop and was I amazed! It's the closest thing to the "make it better" button. Will definitely be useful for concept artists since it's so easy to add details to anything.

15 Deep Visuals That Will Make You Think: 1.

Cuando ves lo que realmente hay en la foto, tu cerebro ya no puede dejar de verlo. No es inmediato, se trata de un fenómeno denominado "atención selectiva" _ La "atención selectiva" se descubrió gracias a un experimento Daniel Simons y Christopher Chabris (1999), conocido como…