Research & Education

Senken is a platform designed to simplify and secure the procurement of high-quality carbon credits for businesses. It employs a rigorous, science-backed standard to evaluate carbon projects, analyzing over 600 data points through its Sustainability Integrity Index (SII). This process ensures that only the top 5% of verified carbon credits are offered, mitigating greenwashing risks and ensuring compliance with standards like Oxford Principles, ICVCM, and CSRD. Senken provides end-to-end support, from strategic advisory and procurement to audit defense with its 'Champion's Kit,' which includes executive summaries, due diligence data, and impact slides for stakeholders. The platform aims to provide transparency and defensibility in carbon compensation, addressing the market reality where many carbon credits fail independent analysis.

Amsterdam Data Science, despite its name, functions as a review platform for online casinos, specifically those not affiliated with the Dutch Cruks exclusion system. The site offers detailed reviews and rankings of various 'No Cruks' casinos, evaluating them based on six key criteria: licensing and reliability, payout speed and limits, bonus conditions, game offerings, payment methods, and customer service. It highlights welcome bonuses, free spins, and other promotional offers, along with minimum deposit requirements and unique features of each casino. The platform aims to guide Dutch players in choosing reliable international online casinos.

+ATLANTIC is a Portuguese Collaborative Laboratory (CoLAB) dedicated to leveraging marine science and ocean technology for sustainable development. The platform focuses on addressing real-world challenges related to oceans, coastal zones, climate, and the environment. It specializes in transforming complex scientific data into practical, actionable products and services. Utilizing advanced AI, data science, and geospatial analysis, +ATLANTIC supports various stakeholders including governance bodies, economic sectors, research institutions, and innovation initiatives across the Atlantic region, promoting a sustainable blue economy. Its work contributes to informed decision-making and innovative solutions for marine and coastal ecosystems.

CEIMIA (Centre d'expertise International de Montréal en intelligence artificielle) is an international center dedicated to the responsible development of AI. It operates on principles of ethics, human rights, inclusion, diversity, innovation, and economic growth. CEIMIA develops and implements high-impact applied projects in responsible AI, focusing on areas such as AI for the environment, AI for health, data governance for AI, and human rights in AI. The organization aims to provide tools and approaches for integrating effective governance and human rights throughout the AI lifecycle, collaborating with various stakeholders to achieve its mission.

awesome-deep-vision is a curated list of deep learning resources specifically tailored for computer vision. Inspired by other 'awesome' lists, it serves as a valuable repository for researchers and practitioners in the field. The repository categorizes resources into various topics such as ImageNet Classification, Object Detection, Object Tracking, Low-Level Vision, Semantic Segmentation, and more. It includes links to academic papers, code implementations, courses, books, videos, and software frameworks, making it a central hub for discovering relevant materials. While the project is not actively maintained, it remains a useful historical reference for foundational and influential works in deep learning for computer vision.

Awesome-Deepfake-Generation-and-Detection is an open-source GitHub repository offering a detailed survey on deepfake generation and detection, specifically focusing on facial manipulation. It encompasses areas such as Face Swapping, Face Reenactment, Talking Face Generation, Face Attribute Editing, and Forgery Detection. The resource also delves into related domains like Head Swap, Face Super-resolution, Face Reconstruction, and Portrait Style Transfer. It aims to be the most comprehensive survey on these topics, providing detailed results for representative works and encouraging contributions from the research community to keep the repository updated with missing papers and new suggestions.

awesome-computer-vision-models is a comprehensive, curated list of popular deep learning models specifically designed for computer vision tasks. This open-source repository serves as a valuable resource for researchers and engineers, offering detailed information on classification, segmentation, and detection models. Each entry includes crucial evaluation metrics such as the number of parameters, FLOPS, and various error rates (e.g., Top-1 Error, Top-5 Error, mIOU), along with the publication year. The repository helps users quickly identify and compare models based on their performance and resource requirements, facilitating informed decisions for their projects. It's an essential reference for anyone working with deep learning in computer vision.

awesome-equivariant-network is a comprehensive, curated list of research papers focused on equivariant neural networks. This GitHub repository is designed as a dynamic resource for academics, researchers, and practitioners interested in the theoretical foundations, applications, and advancements in equivariant neural networks. It categorizes papers into key areas such as Equivariance and Group convolution, Equivariant Density Estimation and Sampling, and various applications. The list is actively maintained and welcomes contributions, ensuring users can stay updated on the latest developments and seminal works in this specialized domain of AI.

awesome-diarization is a comprehensive, curated list of resources dedicated to speaker diarization. This open-source repository organizes a wide array of materials, including academic papers covering various topics like LLM-enhanced diarization, supervised and online diarization, and joint diarization with ASR. It also features a collection of software frameworks and libraries, such as FunASR, SpeechBrain, and pyannote-audio, available in multiple programming languages like Python, Java, and C++. Additionally, it provides information on evaluation tools, clustering algorithms, speaker embedding methods, and relevant datasets, making it an invaluable resource for researchers and developers in speech technology.

Awesome-3D-Object-Detection is a comprehensive curated list of resources dedicated to deep learning for 3D Object Detection, with a strong emphasis on lidar-based methodologies. This GitHub repository serves as an invaluable hub for researchers and engineers, providing direct links to relevant academic papers, associated code implementations, and essential datasets like KITTI, nuScenes, Lyft, and Waymo Open Dataset. It also highlights top conferences and workshops in the field, offering a structured overview of the latest developments and trends. The resource includes surveys, books, videos, and course materials, making it a one-stop reference for anyone looking to delve into or stay current with 3D object detection.

awesome-deep-learning is an extensive, curated list designed for anyone interested in Deep Learning. It serves as a central hub for discovering high-quality educational materials and community resources. The list encompasses a wide array of content, from foundational books and university courses to insightful videos, academic papers, and practical tutorials. It also highlights researchers, relevant websites, datasets, conferences, and various frameworks and tools, making it an invaluable resource for both beginners and experienced practitioners looking to deepen their understanding or find specific information within the Deep Learning domain.

ICCV-2023-25-Papers is an open-source GitHub repository dedicated to collecting research papers from the International Conference on Computer Vision (ICCV) for the years 2023-2025. This tool serves as a valuable resource for researchers, academics, and developers interested in cutting-edge advancements in computer vision and deep learning. It aims to provide easy access to published research, often including associated code and supplementary materials, facilitating further study and development in the field of visual intelligence. The repository is maintained on GitHub, ensuring accessibility and collaborative potential for the academic community.

ResearchBuddy AI is a mobile application designed to empower students and researchers by streamlining their academic journey. It leverages AI to automate literature reviews, generate concise summaries, and facilitate collaboration within virtual research labs. Users can connect with supervisors, receive AI-powered feedback on drafts, and ensure research integrity with verification tools. This comprehensive platform aims to save time and enhance academic output, making the research process more efficient and effective for individuals at various stages of their academic careers.

Awesome-Cybersecurity-Datasets provides a comprehensive, curated list of cybersecurity datasets, making it an essential resource for professionals and researchers in the field. The collection is categorized for easy navigation, including sections for network traffic, malware, web applications, software, URLs & Domain Names, host data, email, fraud, honeypots, binaries, phishing, passwords, and miscellaneous datasets. Each entry typically includes a brief description of the dataset's contents and origin, such as the Unified Host and Network Dataset from Los Alamos National Laboratory or the UNSW-NB15 malware dataset. This resource is particularly useful for those looking to enhance their research, develop new security tools, or train machine learning models for cybersecurity applications.

Daily Academic is an AI-powered platform designed to streamline the discovery of academic research papers. It assists researchers, PhD students, and academics in finding relevant studies, following specific journals, and staying current with new literature in their field. By leveraging AI, the tool provides personalized paper recommendations, helping users navigate the vast amount of scholarly publications efficiently. This platform aims to enhance research productivity and ensure users don't miss critical advancements, making it an indispensable resource for anyone engaged in academic pursuits.

Awesome-AutoDL is a comprehensive, curated list of resources dedicated to Automated Deep Learning, with a particular emphasis on Neural Architecture Search (NAS). This open-source project offers an in-depth analysis of the field, making it an invaluable resource for researchers and practitioners. It compiles links to relevant blogs, AutoDL libraries like PyGlove and AutoGluon, and various benchmarks such as NAS-Bench-101 and NATS-Bench. The list is meticulously organized by publication venues from 2017 to 2021, categorizing papers by type (gradient-based, reinforcement learning, evolutionary algorithm, performance prediction, and others), and includes direct links to code repositories where available. It serves as a central hub for staying updated on the latest advancements and foundational works in AutoDL.

ML & Society at HF is a dedicated platform hosted on Hugging Face Spaces, offering a comprehensive resource for exploring research areas and topics concerning machine learning and its societal impacts. Users can browse various research domains and utilize a built-in search function to efficiently locate specific papers, articles, or code. The platform aims to educate and inform users about critical aspects of AI sustainability, agency, and ecosystems, providing a centralized hub for the Machine Learning and Society team's research.

10x Science provides AI-native software specifically designed for scientists working with protein therapeutics. The platform significantly upgrades peptide mapping, offering processing speeds that are 10 times faster than traditional methods. It is engineered to uncover insights that existing tools might overlook, enhancing the depth and accuracy of protein characterization. A key differentiator is its ability to eliminate the need for file conversions, streamlining the workflow and reducing friction for researchers. This focus on speed, enhanced discovery, and user-friendly integration makes 10x Science a powerful tool for advanced scientific research.

Awesome-Hyperbolic-Representation-and-Deep-Learning is a comprehensive repository of academic papers focusing on hyperbolic embedding, hyperbolic models, and their applications in deep learning. This resource is meticulously organized into categories such as core methods, domain applications, and task-oriented settings, making it easy for researchers to navigate the taxonomy. It highlights the natural advantages of hyperbolic spaces for processing data with tree-like structures or power-law distributions due to their exponential growth property. The repository is continuously updated with the latest research developments, including papers from major conferences like NeurIPS, ICLR, CVPR, and ACL, ensuring users have access to cutting-edge information in the field.

General Vision, Inc. specializes in NeuroMem technology, a neuromorphic memory solution designed for real-time and lifelong learning in AI applications. This technology emphasizes responsible classification and explainability of decisions, making it suitable for edge AI implementations. Key features include adaptive learning, built-in weight correction, high scalability, blazing speed performance, and low power consumption. NeuroMem is applied in smart sensors for edge intelligence and smart storage for local data analysis, supporting tasks like identification, classification, novelty detection, template matching, and target tracking across various industries.

awesome-ml-for-cybersecurity is a comprehensive, curated list of resources dedicated to the intersection of machine learning and cybersecurity. This open-source project serves as a valuable hub for researchers, students, and professionals looking to explore or implement ML techniques for threat detection, prevention, and analysis. The repository categorizes resources into datasets, academic papers, books, conference talks, practical tutorials, and educational courses, making it easy to navigate and find relevant information. It aims to foster development and understanding in this critical domain by providing a centralized collection of high-quality materials.

Falcon-H1-Tiny offers a series of compact language models designed to push the boundaries of AI capabilities at a small scale. These models are available on Hugging Face Spaces and are ideal for research and experimentation. Users can input prompts and receive generated responses from these lightweight but capable AI models, making them suitable for various applications including research paper analysis, data visualization, and the development of small-scale AI applications. The focus on models with 100 million parameters or less makes them particularly efficient and accessible for developers and researchers working with limited resources.

awesome-segment-anything is a comprehensive repository dedicated to tracking and summarizing research progress related to Segment Anything in the field of Computer Vision. It provides a curated list of papers and projects, covering various applications such as medical image segmentation, inpainting, camouflaged object detection, video frame interpolation, and robotics. The repository is continuously updated with the latest breakthroughs, including new models like SAM 3 and EfficientSAM. It serves as a valuable resource for researchers and academics looking to stay informed about developments and applications of Segment Anything.

Awesome-RGBT-Fusion is a comprehensive, open-source collection dedicated to deep learning-based RGB-T fusion methods, codes, and datasets. This resource is invaluable for researchers and developers working in computer vision, particularly those interested in multispectral data. The collection covers key areas such as Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, and RGB-T Fusion Tracking. It provides access to various datasets, tools, and a curated list of academic papers with links to PDFs and code repositories. The project actively encourages contributions, making it a dynamic and evolving hub for advancements in RGB-T fusion.