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by Stanford University · 0 reviews

An inductive representation learning framework for large graphs that samples and aggregates node features from local neighborhoods, enabling embeddings for unseen nodes.

by Mila / University of Cambridge · 0 reviews

A neural network architecture using masked self-attention on graph-structured data, learning different weights for different neighbor nodes.

by Amazon Web Services · 0 reviews

A framework for deep learning on graphs supporting PyTorch and TensorFlow, with optimized message passing primitives and distributed training.

by Microsoft Research · 0 reviews

Deep residual learning network architecture that enables training of very deep neural networks through skip connections for image classification tasks.

by Google Research · 0 reviews

Family of convolutional neural networks that use compound scaling to uniformly scale depth, width, and resolution for efficient image classification.

by Google Research · 0 reviews

Lightweight family of convolutional neural networks designed for efficient inference on mobile and embedded devices with minimal computational overhead.

by Google Research · 0 reviews

Deep convolutional neural network trained on ImageNet for classifying images into object categories with high accuracy.

by Meta AI · 0 reviews

Foundation model for promptable visual segmentation in images and videos, enabling real-time object tracking and zero-shot generalization to unseen content.

by Google Research · 0 reviews

Applies transformers directly to image patches for classification; inspired many CV variants.

by Ultralytics · 0 reviews

The latest YOLO family model for real-time object detection, classification, and segmentation.

by Meta AI · 0 reviews

A promptable zero-shot image segmentation model trained on 1.1B masks over 11M images.