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A comprehensive repository covering papers, codes, datasets, tutorials, and applications for transfer learning, domain adaptation, and more.
A comprehensive reading list for research topics in multimodal machine learning.
A self-supervised learning framework for computer vision tasks with a focus on contrastive learning.
A toolkit for self-supervised speech pre-training and representation learning.
PyTorch implementation of SimCLR, a simple framework for contrastive learning of visual representations.
CodeSearchNet provides datasets, tools, and benchmarks for representation learning of code, enabling AI-powered code discovery.
A powerful BERT-based language model for natural language understanding and representation learning.
Cambrian-1 is a multimodal LLM with a vision-centric design for building AI-powered chatbots and applications.
A comprehensive collection of research on knowledge graphs, covering various applications and techniques.
A powerful vision-language pre-training method for tasks like image-text retrieval and captioning.
Unsupervised image classification library using contrastive learning and SCAN algorithm.
A library for self-supervised learning on graphs, providing contrastive, generative, and predictive pretext tasks.
Unified Training of Universal Time Series Forecasting Transformers for accurate time series forecasting.
Audio Spectrogram Transformer (AST) for audio classification and representation learning tasks.
A comprehensive list of contrastive self-supervised learning papers for machine learning researchers.
A high-performance graph embedding system that supports CUDA and can be used for machine learning and knowledge graph applications.
Multimodal AI toolkit for fast content understanding and generation across text, images, and video
A bio-computing platform for large-scale representation learning and multi-task deep learning on molecular and biological data.
An open-source toolkit for urban spatial-temporal data mining and traffic prediction tasks.
A general representation model for cross-modal learning across vision, audio, and language.
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