FSD without convnets

 Vision Transformers compared with CNN and its need for large datatset and their inductive biases. Swin (shifted window) ViT.

"CNN is even backbones behind some of the non-grid signal processing networks like equivariant nn, graphCNN and pointnet for point cloud etc."

Alternate and hybrid architectures possibly being used by Tesla FSD instead of CNNs.

A whole-slide foundation model for digital pathology from real-world data - GigaPath, a novel vision transformer for pretraining large pathology foundation models on gigapixel pathology slides. 

Building human level intelligence with neuroanatomy approach or the parts of the brain approach where you build  artificial cerebral cortex as in

"1.LLMs are basically the prefrontal cortex. 2.Tesla built something akin to a parietal and occipital cortex."

Lecture 14 - vision transformers from efficientml.ai MIT course.

VILA-1.5, an efficient visual language model (VLM) that can understand not only images but also videos.

Apple's Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum tries to avoid a limitation of current LLM - Fixed token usage regardless of problem difficulty. fixed token usage regardless of problem difficulty

Efficient ML Harvard seminar - learning to learn