Google Gemini, a multimodal AI by DeepMind, processes text, audio, images, and more. Gemini outperforms in AI benchmarks, is optimized for varied devices, and has been tested for safety and bias, adhering to responsible AI practices.
Learning on Graphs with Out-of-Distribution Nodes
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs.
GitHub Link
The GitHub link is https://github.com/songyyyy/kdd22-oodgatIntroduce
The repository "SongYYYY/KDD22-OODGAT" contains the implementation of "OODGAT," a method presented in the paper "Learning on Graphs with Out-of-Distribution Nodes." The implementation involves running scripts within a 'pyg_data' folder and requires dependencies like torch_geometric, networkx, sklearn, numpy, and scipy. The article provides instructions for downloading datasets, but notes changes in directory structure for recent versions of torch_geometric. The code supports testing on various datasets by modifying parameters in the 'train.py' script, including dataset names, splits, and hyperparameters. The hyperparameters for different datasets are listed in a table. Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs.Content
This is the repository for paper 'Learning on Graphs with Out-of-Distribution Nodes'. To run the scripts, please create a folder named 'pyg_data' in the root directory. In the first run, datasets will be automatically downloaded to the folder from torch_geometric. When dataset is changed, the 'splits', 'ID_classes' and 'continuous' should also be changed accordingly. Hyperparameters for all datasets are listed in the following table:
Alternatives & Similar Tools
Video ReTalking-focuses on audio-based lip synchronization for talking head video editing Open Source
Video ReTalking, advanced real-world talking head video according to input audio, producing a high-quality
UniSim-Chat Control Video and Virtual simulation Open Source
Then transplant it to the real world to solve complex problems
LongLLaMA is a large language model designed to handle very long text contexts, up to 256,000 tokens. It's based on OpenLLaMA and uses a technique called Focused Transformer (FoT) for training. The repository provides a smaller 3B version of LongLLaMA for free use. It can also be used as a replacement for LLaMA models with shorter contexts.
Large Language and Vision Assistant
Use bank data and Ntropy's AI. Parse bank feeds and statements, extract revenue and COGs, automatically re-create a P&L within milliseconds. Any industry, any geo.