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Pattern Analysis of various datasets by COMP3710 students in 2025 at the University of Queensland
Official code for "No time to train! Training-Free Reference-Based Instance Segmentation"
Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks
Reverse Engineering the Abstraction and Reasoning Corpus
A repo to evaluate various LLM's chess playing abilities.
Deep learning examples for the Instant Super Computer
Pattern Analysis of various datasets by COMP3710 students at the University of Queensland
Code for the Paper : NBC-Softmax : Darkweb Author fingerprinting and migration tracking (https://arxiv.org/abs/2212.08184)
The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
soccerapi, an unambitious soccer odds scraper ⚽️
Experiments on GPT-3's ability to fit numerical models in-context.
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
Topological botnet detection datasets and graph neural network applications
A minimal extension which demonstrates how to create a extension with Gmail.JS and NodeJS-based bundling.
A ChatGPT Chrome extension. Integrates ChatGPT into every text box on the internet.
Code for the Paper : Efficient block contrastive learning via parameter-free meta-node approximation
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
Pytorch implementation of Influence Augmented Contrastive (IAC) loss and SCGC : Self-Supervised Contrastive Graph Clustering (https://arxiv.org/abs/2204.12656)
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Doppelganger-finder finds multiple accounts (doppelgangers) of a user.
A pytorch implementation of H2GCN raised in the paper "Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs".
Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"
This repository is an implementation for the loss function proposed in https://arxiv.org/pdf/2110.06848.pdf.
Pytorch implementation of FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping (https://arxiv.org/abs/2110.11464)