HumanAIze

Where Data Meets Human Precision

[HumanAIze @HumanAIze_]

💻 https://t.co/MtQjnYwANF
📨 [email protected]

#MachineLearning #data #medicaldata #deeplearning #computervision #AI #ArtificialInteligence #DataScience #PredictiveAnalytic #AIinHealthcare #HealthcareAI #BigData #HITsm #Cardiology

[HumanAIze @HumanAIze_]

Explore our collection of CT & MRI datasets on Kaggle perfect for your next #AI, #ML, or #DeepLearning project in #Radiology & medical research!
📂 Start building smarter models with real-world data.
🔗 https://t.co/gK8nujDHRB

#DataScience #MedicalImaging #ArtificialIntelligence #HealthcareAI #MedTech #ComputerVision #OpenData #BigData #NeuroAI #RadiologyAI

[HumanAIze @HumanAIze_]

Ready to build smarter AI solutions? Get access to diverse, high-quality medical datasets now! 🔗 https://t.co/MOGhoJ08Sf

[HumanAIze @HumanAIze_]

🚀 Train smarter, not harder! Unlock high-quality medical imagery datasets for AI development.
📥 Free sample available here 👉 https://t.co/Drib8gldj4
🔍 Precision, diversity, and compliance—all in one place. #AI #MedicalData #HealthTech

[HumanAIze @HumanAIze_]

Get access to diverse, high-quality medical datasets now! 🔗 https://t.co/MOGhoJ08Sf

[Rudy Gilman @rgilman33]

The VAE used in SDXL has extremely high-magnitude "splotches" in its latents. The individual neurons in these blobs fire with magnitudes of close to a million.

These aren't some accident of training or initialization—the model creates these high-magnitude splotches for a specific reason: to circumvent the group-norm operations.

[Alec Helbling @alec_helbling]

This animation shows how a uniform grid of points is deformed by a flow matching model.

The visualization highlights how the learned flow warps the underlying space—stretching, compressing, and bending it—to transform one probability distribution into another.

[Alexandre Morgand @Almorgand]

DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion

TL;DR: pixel-wise ray origins and endpoints in a global frame; denoising diffusion process; patch-wise embeddings with DINOv2 and embed noisy ray origins and endpoints into latents (1/2)

[Tom Yeh @ProfTomYeh]

Can you calculate a Vision Transformer (ViT) by hand? ✍️ If you can, come join me next week at the LLM Paper Club. I will extend ViT to Llama 1 -> 2 -> 3 -> 4 live. Event link below.

[HumanAIze @HumanAIze_]

🚀 Medical Datasets by HumanAIze for ML projects

🧠 Brain MRI
🔗 https://t.co/vG1M1j4ym1

❤️ Heart CT & MRI
🔗 https://t.co/CTYRMONWZW

🦴 Pelvic MRI
🔗 https://t.co/Yv5jPxxuzM

🎗️ Breast Cancer MRI
🔗 https://t.co/oP1gcADzRk

#AI #MachineLearning #HealthcareAI #DataScience

[ℏεsam @Hesamation]

transformers vs. mixture of experts visually explained:

[Alec Helbling @alec_helbling]

Flow Matching aims to learn a "flow" that transforms a simple source distribution (e.g. Gaussian) to an arbitrarily complex target distribution.

This video shows the evolution of the marginal probability path as a source distribution is transformed to a target distribution.

[HumanAIze @HumanAIze_]

Get access to diverse, high-quality medical datasets now! 🔗 https://t.co/MOGhoJ08Sf

[HumanAIze @HumanAIze_]

Ready to build smarter AI solutions? Get access to diverse, high-quality medical datasets now! 🔗 https://t.co/MOGhoJ08Sf

[HumanAIze @HumanAIze_]

🚀 Train smarter, not harder! Unlock high-quality medical imagery datasets for AI development.
📥 Free sample available here 👉 https://t.co/Drib8gldj4
🔍 Precision, diversity, and compliance—all in one place. #AI #MedicalData #HealthTech