Data-Designer
Follow
Starred repositories
The official implementation of Achieving Cross Modal Generalization with Multimodal Unified Representation (NeurIPS '23)
Latest progresses (papers & datasets) of zero-shot learning.
📚 A collection of resources and papers on Vector Quantized Variational Autoencoder (VQ-VAE) and its application
A curated list of zero-shot captioning papers
A Comprehensive List of Works for Generative Modeling with Limited Data, Few Shots, and Zero Shot
Official PyTorch implementation Source code for MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation, accepted at SIGIR 2023
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
A curated list of awesome self-supervised methods
Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
A curated list of awesome Diffusion notebooks, tools, software, tutorials and resources.
Benchmark for Multi-Scenario-Recommendation.
Multi-domain Recommendation with Adapter Tuning
This is a library built upon RecBole for cross-domain recommendation algorithms
List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.
A curated list of papers and resources based on "Large Language Models on Graphs: A Comprehensive Survey".
A curated list of awesome Multimodal studies.
A curated list of awesome resources about multimodal recommender systems.
A curated list of papers on cold-start recommendations.
SIGIR 2024, Sequential recommendation, Attribute-Opinions, Fine-grained User and Item Representation.
A curated list of papers for multi-label classification.
Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.
Heart disease prediction and Kidney disease prediction. The whole code is built on different Machine learning techniques and built on website using Django
[CIKM2023] Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction by Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang, Changlong Sun, and Xiaozhong Liu
