Research & Education

awesome-relation-extraction is a comprehensive, open-source curated list of resources dedicated to Relation Extraction, a crucial task in Natural Language Processing (NLP). This repository, inspired by other 'awesome' lists, compiles a wide array of research trends, surveys, and papers covering supervised, distant supervision, GNN-based, and language model approaches. It also features knowledge graph-based and few-shot learning methods. Additionally, the resource includes links to relevant datasets, videos, lectures, systems, and frameworks, making it an invaluable tool for researchers and practitioners looking to explore or advance their work in relation extraction.

awesome-text-summarization is a comprehensive Open Source guide designed to help users understand and implement various text summarization techniques. It delves into fundamental approaches such as extractive summarization, which selects key phrases from the original text, and abstractive summarization, which generates new sentences to convey the main ideas. The guide also explores combination approaches and the application of transfer learning in summarization. Beyond methodologies, it offers valuable resources including evaluation metrics, relevant datasets, useful libraries, academic articles, and research papers, making it an invaluable resource for anyone looking to tackle text summarization tasks.

awesome-text-to-image-studies is a comprehensive GitHub repository dedicated to summarizing papers and resources related to text-to-image (T2I) generation. This tool organizes academic studies based on various research directions, publication years, and conferences, making it an invaluable resource for researchers and academics. It includes sections on survey papers, conditional T2I generation, personalized T2I generation, and text-guided image editing. The repository also features a list of off-the-shelf T2I generation products and toolkits, along with a detailed 'To-Do Lists' section for future updates, ensuring it remains current with the latest advancements in the field. Users can find links to papers, project pages, and code where available, facilitating deeper exploration of the studies.

Plattform Lernende Systeme is a prominent German AI platform and expert network dedicated to advancing the understanding and responsible application of Artificial Intelligence. It brings together nearly 200 members from scientific research, industry, and civil society to facilitate interdisciplinary exchange and public dialogue on AI topics. The platform's core mission involves developing position papers on the opportunities and challenges presented by AI, as well as formulating recommendations for its ethical and effective deployment. Established in 2017 by the German Federal Ministry of Education and Research, it serves as a crucial hub for national AI strategy, promoting research, innovation, and transfer into practical applications across various sectors. The platform also monitors AI developments in Germany, showcases success stories, and provides educational resources.

Awesome-GPTs is a comprehensive, open-source GitHub repository featuring a vast collection of over 1000 GPTs, categorized into 10 distinct groups. This resource also includes more than 80 leaked prompts, offering valuable insights and examples for users interested in GPT applications. The project aims to provide a centralized hub for discovering and understanding diverse GPT implementations, making it a useful tool for developers, researchers, and AI enthusiasts. Its community-driven nature encourages contributions and continuous expansion of the collection, fostering an environment for shared knowledge and exploration within the AI community.

awesome-graph-classification is a comprehensive collection of graph classification methods, encompassing embedding, deep learning, graph kernel, and factorization papers. This resource provides researchers and practitioners with a curated list of important papers, often accompanied by their reference implementations. It serves as a valuable starting point for exploring various techniques in graph-based machine learning, offering insights into areas like network embedding, graph convolutional networks, and graph attention networks. The repository also links to relevant graph classification benchmark datasets, making it a practical tool for academic research and development in the field.

awesome-uncertainty-deeplearning is an extensive open-source repository dedicated to predictive uncertainty estimation in deep learning models. It compiles a wide range of resources including surveys, academic papers, datasets, and code implementations. The collection covers various methodologies such as Bayesian methods, ensemble techniques, sampling/dropout-based approaches, post-hoc methods, data augmentation, and evidential deep learning. It also addresses applications in classification, regression, object detection, natural language processing, and more. This repository is an invaluable resource for researchers and practitioners looking to explore, understand, and implement uncertainty quantification in their deep learning projects.

Awesome-diffusion-model-for-image-processing is a comprehensive, open-source GitHub repository that serves as a summary of diffusion model-based image processing techniques. It covers a wide array of applications such as image restoration, enhancement, coding, and quality assessment. The repository is continuously updated with new related works and includes detailed sections on image super-resolution, video restoration, inpainting, denoising, dehazing, deblurring, and medical image restoration. It also features benchmarks, datasets, and models for image/video compression and quality assessment, making it an invaluable resource for researchers and practitioners in the field.

Awesome-CV-MasterHub is an open-source repository providing a curated list of recent Computer Vision (CV) papers. It serves as a valuable resource for researchers and practitioners looking to stay abreast of the latest developments in the field. The platform organizes papers by various CV sub-domains such as Image Classification, Object Detection, Semantic Segmentation, Image Generation, and Vision-LLMs. Users can easily browse through the list and find links to papers, with code links provided where available. The repository is actively maintained, with updates to ensure the most recent and relevant articles are included, typically retaining up to 200 papers per area. It encourages community contributions through issues and pull requests for any overlooked papers.

Awesome-LLMs-as-Judges is a dedicated repository for researchers and practitioners interested in the field of Large Language Models (LLMs) as evaluators. It serves as the official companion to the survey paper "LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods," offering a continuously updated collection of relevant academic papers. The repository categorizes papers by functionality (performance evaluation, model enhancement, data collection), methodology (single-LLM, multi-LLM, human-AI collaboration systems), application areas, and meta-evaluation. It also includes a "Daily Papers on LLMs-as-Judges" section, automatically retrieving and updating the latest arXiv papers, making it a one-stop resource for staying current with advancements in this rapidly evolving domain.

Climate AI Nordics is a collaborative network of researchers dedicated to leveraging AI technologies to tackle the urgent global challenge of climate change. The network focuses on creating and promoting AI solutions that support both climate change mitigation, aiming to reduce its severity, and adaptation, helping societies adjust to its effects. Recognizing the current climate emergency, Climate AI Nordics emphasizes a multifaceted approach, including policy change, limiting activities contributing to climate change, and bolstering societal resilience. The initiative brings together researchers from Nordic countries to accelerate progress in this critical field, facilitating the sharing of expertise and resources to develop impactful AI solutions for a sustainable future.

awesome-llm-interpretability is a comprehensive, curated list of resources dedicated to understanding and interpreting Large Language Models (LLMs). This open-source GitHub repository serves as a central hub for researchers and practitioners, offering a wide array of tools, academic papers, insightful articles, and community links. It covers various aspects of LLM interpretability, from visualization platforms like The Learning Interpretability Tool and Comgra, to specific research on attention mechanisms, neuron behavior, and factual associations within models. The resource is invaluable for anyone looking to delve into the inner workings of LLMs, debug neural networks, analyze training processes, or explore mechanistic interpretability.

Awesome-LLM-Long-Context-Modeling is a comprehensive, open-source GitHub repository dedicated to curating essential papers and blogs focused on Large Language Model (LLM) based long context modeling. This resource is designed for researchers and practitioners who need to stay abreast of the latest advancements in enabling LLMs to process and understand extended sequences of text. It provides a structured collection of academic papers and insightful blog posts, making it easier to navigate the rapidly evolving landscape of long-context LLMs. The repository is freely available, offering a valuable knowledge base for anyone working on or interested in the challenges and solutions related to long-term memory and context handling in LLMs.

Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. It integrates cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling scientists to dramatically enhance research productivity and generate testable hypotheses. Biomni supports various LLM providers like Anthropic, OpenAI, Azure OpenAI, Gemini, and Groq, and can be configured via environment variables or a .env file. It features a data lake for biomedical information, a Gradio interface for interactive use, and configuration management for consistent settings. Additionally, Biomni can generate PDF reports of execution traces, supports Model Context Protocol (MCP) for external tool integration, and includes a Know-How Library of best practices. It also offers Biomni-R0, a specialized reasoning model for biology, and Biomni-Eval1, a comprehensive evaluation benchmark.

Cam2BEV offers a TensorFlow implementation for generating semantically segmented Bird's Eye View (BEV) images from the input of multiple vehicle-mounted cameras. This open-source methodology addresses the challenge of distance estimation in monocular camera systems by transforming perspectives into a BEV. Unlike traditional Inverse Perspective Mapping (IPM) which distorts 3D objects, Cam2BEV provides a corrected 360° BEV image, segmenting it into semantic classes and predicting occluded areas. The neural network approach is trained on synthetic datasets, enabling it to generalize effectively to real-world data without relying on manual labeling. It supports DeepLab and uNetXST architectures and includes preprocessing techniques for handling occlusions and projective transformations, making it a valuable resource for research in automated driving.

北大法宝V6 (PKULAW) is a comprehensive legal information retrieval system developed by 北京北大英华科技有限公司. It boasts a vast database of over 5 million legal documents, including laws, regulations, judicial cases, and legal journals, sourced from authoritative bodies recognized by China's Legislative Law. The platform updates daily with thousands of new entries, ensuring users have access to the most current legal information. Key features include advanced search with multiple logical combinations, subscription pushes for tailored content, and AI-powered tools like "律AI多" for intelligent retrieval and "法宝AI" for document analysis. It also offers specialized databases for various legal fields such as IP, labor law, and criminal law, making it an essential tool for legal professionals and researchers.

Change-Detection-Review is an open-source resource offering a detailed review of artificial intelligence-based change detection methods, particularly within the domain of remote sensing. This GitHub repository compiles available codes and open datasets essential for deep learning applications in this field. It is based on the paper "Change detection based on artificial intelligence: state-of-the-art and challenges," providing insights into the implementation processes, data types (optical RS, SAR, street view, heterogeneous data), and general frameworks of AI-based change detection. The review also covers commonly used networks, application domains, and discusses major challenges and future prospects, making it a valuable resource for researchers.

Baichuan-7B is a large-scale 7B parameter pre-training language model developed by BaiChuan-Inc. Based on the Transformer structure, it was trained on approximately 1.2 trillion tokens and supports both Chinese and English languages. The model features a context window length of 4096 and has demonstrated strong performance on standard Chinese and English benchmarks like C-Eval and MMLU. It includes optimizations for training stability and throughput, such as efficient operators, operator splitting, mixed precision, and communication optimizations, achieving high GPU peak compute utilization. The model also features an optimized tokenizer for Chinese language compression and improved mathematical capabilities.

Contrastive-Learning-NLP-Papers is an open-source GitHub repository offering a comprehensive list of research papers focused on contrastive learning techniques within Natural Language Processing (NLP). This resource is designed for researchers and practitioners interested in representation learning, a core component of modern NLP models. The collection covers foundational concepts, sampling strategies, notable applications, and detailed analyses of contrastive learning. It also includes sections dedicated to contrastive learning specifically for NLP tasks such as text classification, sentence embeddings, machine translation, and data augmentation, providing a structured overview of the field's advancements.

Data-science-ipython-notebooks is an extensive Open Source collection of Python notebooks designed for data science education and practical application. It covers a wide array of topics including deep learning with frameworks like TensorFlow, Theano, Caffe, and Keras, as well as machine learning with scikit-learn. The collection also delves into big data technologies such as Spark, Hadoop MapReduce, and HDFS. Users can find notebooks dedicated to data visualization using matplotlib and pandas, along with essential Python programming concepts, AWS, and various command-line tools. This resource is ideal for students and professionals looking to learn and apply data science techniques through hands-on examples and tutorials.

Tilburg.ai, featuring 'Tilly' the AI chatbot, is designed specifically for higher education, empowering learning, teaching, and collaboration. It allows university students and staff to get instant answers to questions, study smarter with AI-powered explanations, and discover an AI platform built for academic use. Users can log in with their university accounts to access chatbots that respond based on uploaded course materials like lectures, textbooks, and academic papers. A key differentiator is its commitment to data privacy, ensuring conversations remain within a secure environment and are not used for external model training. The platform also provides source citations for all answers, enhancing transparency and reliability.

Cookbook, developed by EleutherAI, serves as a comprehensive resource for individuals delving into deep learning, particularly those new to the field. It offers practical details and essential utilities for effectively working with real-world models. The resource includes introductory materials on transformers, making complex concepts accessible. Key sections cover calculations for training and inference (such as FLOPs, memory overhead, and parameter count), benchmarks for communication and transformer sizing, and a curated reading list. It also provides best practices for distributed deep learning and guidance on data/model directories, making it an invaluable guide for both learning and practical application.

Deep-Learning-Paper-Review-and-Practice is an open-source GitHub repository dedicated to providing comprehensive reviews and practical code implementations for deep learning papers. The repository curates a selection of recent and highly influential deep learning research, categorized into areas such as Image Recognition, Natural Language Processing, Generative Models & Super Resolution, Modeling & Optimization, and Adversarial Examples & Backdoor Attacks. Each paper entry includes links to the original paper, a video review, a summary PDF, and corresponding code practices, making it an invaluable resource for understanding and applying cutting-edge deep learning techniques. Users can engage with the content by exploring detailed explanations and hands-on coding examples, fostering a deeper understanding of complex AI concepts.

Spain AI is an association founded in 2017 with the mission to make Artificial Intelligence accessible to everyone in Spanish-speaking countries. It serves as a hub for both enthusiasts and professionals, offering a variety of resources including training programs for individuals, companies, and collectives, as well as organizing events, workshops, and hackathons. The platform fosters a community of over 10,000 members, connecting individuals from business and academic sectors. Spain AI also provides a newsletter to keep its community informed about the latest news, events, and job opportunities in the AI field.