_graph-neural-networks-gnn-using-pytorch-geometric-124-stanford-university-preview-1.jpg "Graph Neural Networks (GNN) using Pytorch Geometric | Stanford University from pytorch geometric Video Screenshot Preview 1")
Gallery_graph-neural-networks-gnn-using-pytorch-geometric-124-stanford-university-preview-3.jpg "Graph Neural Networks (GNN) using Pytorch Geometric | Stanford University from pytorch geometric Video Screenshot Preview 3")
Gallery_graph-neural-networks-gnn-using-pytorch-geometric-124-stanford-university-preview-hqdefault.jpg "Graph Neural Networks (GNN) using Pytorch Geometric | Stanford University from pytorch geometric Video Screenshot Preview hqdefault")
GalleryPlay Video:
(Note: The default playback of the video is HD VERSION. If your browser is buffering the video slowly, please play the REGULAR MP4 VERSION or Open The Video below for better experience. Thank you!)
Jump To Video Parts
⏲ Duration: 1 hour 14 minutes 23 seconds
👁 View: 51.2K times
Play Audio:👁 View: 51.2K times
Description:
Lindsey AI
Lindsey AI
Share with your friends:
Whatsapp | Viber | Telegram | Line | SMS
Email | Twitter | Reddit | Tumblr | Pinterest
Related Videos
_physics-based-differentiable-rendering-a-comprehensive-introduction.webp "Physics-based rendering algorithms generate photorealistic images by simulating the flow of light through a detailed mathematical representation of a virtual scene. In contrast, physics-based differentiable rendering algorithms focus on computing derivative of images exhibiting complex light transport effects (e.g., soft shadows, interreflection, and caustics) with respect to arbitrary scene parameters such as camera pose, object geometry (e.g., vertex positions) as well as spatially varying mat")
_how-to-use-edge-features-in-graph-neural-networks-and-pytorch-geometric.webp "DeepFindr")
⏲ 16 minutes 48 seconds 👁 23.7K
_cvpr-2021-tutorial-physics-based-differentiable-rendering.webp "Physics-based rendering algorithms generate photorealistic images by simulating the flow of light through a detailed mathematical representation of a virtual scene. In contrast, physics-based differentiable rendering algorithms compute derivatives of images exhibiting complex light transport effects (e.g., soft shadows, interreflections, multiple scattering, and caustics) with respect to arbitrary scene parameters (e.g., camera pose, object geometry, spatially-varying material properties). nnThi")
_gnn-project-2-creating-a-custom-dataset-in-pytorch-geometric.webp "DeepFindr")
_pytorch-geometric-tutorial-introduction-to-pytorch-geometric.webp "Antonio Longa")
_jens-agerberg-teaching-neural-networks-a-sense-of-geometry.webp "By taking neural networks back to the school bench and teaching them some elements of geometry and topology we can build algorithms that can reason about the shape of data. Surprisingly these methods can be useful not only for computer vision – to model input data such as images or point clouds through global, robust properties – but in a wide range of applications, such as evaluating and improving the learning of embeddings, or the distribution of samples originating from generative models.")
_physics-based-differentiable-rendering-a-comprehensive-introduction.jpg)
_how-to-use-edge-features-in-graph-neural-networks-and-pytorch-geometric.jpg)
_cvpr-2021-tutorial-physics-based-differentiable-rendering.jpg)
_gnn-project-2-creating-a-custom-dataset-in-pytorch-geometric.jpg)
_pytorch-geometric-tutorial-introduction-to-pytorch-geometric.jpg)
_jens-agerberg-teaching-neural-networks-a-sense-of-geometry.jpg)
![[+]](/images/menu.gif){kind=small}