This repository is a comprehensive, curated bibliography and knowledge base for the field of active learning. It organizes hundreds of research papers by conference (ICML, NeurIPS, CVPR, etc.), journal (JMLR, TPAMI, etc.), thesis, and content topic (problem settings, deep active learning, applications, etc.). The repository also provides taxonomies, summaries, and links to papers. It is not a software application or data pipeline; it is a structured reference list intended for researchers and practitioners. The content is entirely in Markdown files, making it trivially reproducible and easy to navigate. The repository is actively maintained with recent commits and a growing star count.
HeyForm is an open-source form builder and management platform designed for small businesses. It provides a full-stack solution with a React-based drag-and-drop form builder, a NestJS GraphQL API backend, a standalone form renderer for public-facing forms, and an embed script for third-party sites. The system supports a wide range of field types (text, choice, rating, payment, signature, etc.), conditional logic with branching, multi-language internationalization, Stripe payments, AI-assisted form generation via OpenAI, and integrations with popular services like Slack, Notion, and Google Sheets. The architecture follows a monorepo pattern with shared packages for answer validation, utility functions, and type definitions, enabling code reuse across the server, webapp, and renderer. The backend is built with NestJS, MongoDB (Mongoose), Redis (Bull queues), and Apollo GraphQL, with scheduled jobs for cleanup and async task processing. The frontend uses React 19, Tailwind CSS, Zustand for state management, and Apollo Client for GraphQL communication. Security is a strong focus: the codebase includes SSRF protection, upload content validation, rate limiting, atomic verification codes, and extensive input sanitization. The project has a comprehensive test suite covering unit, integration, and end-to-end scenarios, including a permission matrix with over 270 cases. Despite some duplicated logic and large locale files, the overall code quality is high, with well-structured modules and clear separation of concerns.
This is a monorepo for SurveyJS, a comprehensive open-source form/survey library that renders dynamic JSON-based forms across multiple frontend frameworks (React, Angular, Vue 3, and plain JavaScript). The core engine (`survey-core`) handles model parsing, validation, expressions, triggers, input masks, drag-and-drop, theming, and localization. It provides 20+ question types (text, checkbox, radio, dropdown, matrix, ranking, file, signature, etc.) with extensible UI components via framework-specific rendering packages. The library also includes a built-in linter for survey JSON static analysis and a tester for programmatic survey test execution. Architecture follows a layered pattern: core logic separate from UI, with each framework package consuming the core via a shared set of exported model classes. The codebase is large (2896 files) but well-structured, featuring a custom expression parser (PEG), a modular action-bar system, advanced popup management with focus trapping, and a generic drag-drop engine. Maintenance is active with frequent releases, but there is some technical drift in the form of commented-out legacy testcafe tests, auto-generated theme files, and cross-framework duplication that requires careful synchronization.
This repository is a comprehensive survey paper on the intersection of knowledge graphs and multi-modal learning. It organizes the literature into two main directions: KG-driven multi-modal learning (KG4MM) and multi-modal knowledge graphs (MM4KG). The survey covers various tasks such as visual question answering, image classification, multi-modal named entity recognition, and knowledge graph completion, among others. Each section includes a list of relevant papers with links to their sources (e.g., arXiv, conference proceedings). The repository is maintained through updates that include adding new papers and fixing links, as evidenced by the recent commit in 2026 and the news items up to 2024-12.
Matterpoll is a Mattermost plugin that enables users to create and manage polls directly within the chat platform. It provides a slash command (`/poll`) and interactive dialogs for creating polls with options like multi-vote, anonymous voting, progress display, and public add-option. The server-side is written in Go, handling HTTP API endpoints, slash commands, and poll state persistence via Mattermost's KV store. The webapp component, built with React and TypeScript, renders rich poll attachments, a right-hand side card, and handles user interactions. The plugin supports 11 languages via go-i18n and follows Mattermost's plugin development patterns with comprehensive testing and CI/CD. It is a well-structured, production-quality plugin that demonstrates clean separation of concerns, testable interfaces, and localization best practices.
This repository is a curated survey of research papers on World Action Models (WAM), maintained as a living list in a single README.md file. It categorizes 109 papers into three families: Render-and-Decode, Latent-Only, and Video-Generation-Free, with each entry including publication date, title, backbone, substrate, and a one-line summary. The repository serves as a reference resource for researchers in robotics and embodied AI, providing a structured overview of recent advances in WAM methodologies. While it contains no executable code or data processing pipelines, it is actively updated via community contributions through pull requests.
This repository is an awesome list curating research papers on Video-LMM Post-Training for video reasoning. It maintains a JSON dataset of paper metadata (title, release date, links, tags, etc.) and uses a Python script to generate a categorized README.md with markdown tables organized by research paradigm (Reinforced Video-LMMs, SFT for Reasoning, Test-Time Scaling, Benchmarks, Surveys). The repository serves as a reference tracker for the latest advancements in enhancing reasoning capabilities of video-focused multimodal models through post-training techniques. The hybrid nature lies in its dual role as both a data collection (the JSON paper metadata) and a lightweight data presentation system (the generation script).
This repository is a curated list of academic papers on learning-based odometry, covering visual odometry (VO), visual-inertial odometry (VIO), inertial odometry, LiDAR odometry, semantic SLAM, and NeRF SLAM. It is organized as a README with tables detailing paper titles, venues, years, links to papers and code repositories, and short notes on each method. The list includes papers from 2015 to 2026 and is regularly updated with recent contributions. While no code or datasets are provided, the README serves as a comprehensive bibliography for researchers and practitioners in the field. It links to numerous external GitHub repositories and blogs for further exploration.
This repository is a curated list of papers, datasets, and code repositories for multi-turn interactions with large language models, accompanied by a static website that serves as a companion to a survey paper published in Transactions on Machine Learning Research (TMLR) 2026. The website includes sections on tasks (instruction following and conversational engagement), improvement methods, open challenges, and a searchable benchmark explorer. The repository also includes a script for processing survey data from Excel files and the README, though this script is not required to run the website.
This repository is a curated list of research papers and resources on editing techniques for Neural Radiance Fields (NeRF) and 3D Gaussian Splatting. It contains hundreds of entries organized by year and venue, covering seminal works, surveys, and recent advances in 3D scene editing, stylization, inpainting, relighting, and more. The repository is maintained by Weize Li and collaborators from multiple institutions. It serves as a comprehensive index for researchers and practitioners, but contains no executable code, libraries, or data pipelines. It is purely a reference list.
This repository is a curated collection of research papers on Multi-Agent Reinforcement Learning (MARL), organized by categories such as framework, cooperation, communication, and applications. It serves as a reference for researchers and practitioners in the field. The repository contains no code, data, or executable components; it is purely a markdown file with links to papers. The list is actively maintained and includes recent papers up to 2025. It is not a software application or data pipeline, but a static paper list.
This repository is a curated collection of machine unlearning research, combining a comprehensive survey paper (in the form of a Kaggle notebook) with executable implementations of several unlearning algorithms across different domains. It includes code for graph neural network unlearning (GraphProjector), federated learning unlearning (FedEraser), and approximate nearest neighbor search with unlearning (UnfoldedSelf-ReconstructionHashing, a fork of ann-benchmarks). The repo serves both as a literature index (taxonomy, evaluation metrics, datasets) and a benchmark hub for reproducing and comparing unlearning methods. While the survey is actively maintained, the algorithm codebases are research prototypes with varying levels of polish and portability.
This repository is a curated resource list for deep learning-based face restoration algorithms, organized as a survey. It contains no executable code, data processing scripts, or model implementations. Instead, it provides structured tables categorizing research papers by year, publication, title, links, and architecture across tasks like blind face restoration, super-resolution, deblurring, denoising, and artifact removal. The README.md serves as the primary documentation, supplemented by illustrative GIFs in the figures directory. While valuable as a reference, it lacks any reusable components or reproducible research elements since it solely aggregates external links without original code, data, or experiments.
This repository is a curated collection of research papers on machine learning systems, organized by topic into directories such as Checkpoint&Memory, Cluster Scheduling, Serving, and Training. Each directory contains PDF files of relevant papers, often accompanied by README.md files providing context or summaries. The repository serves as a reference literature review for researchers and practitioners in the field of ML systems, covering topics like checkpointing, cluster scheduling, model serving, training optimizations, and more. It does not contain any source code or executable components, focusing solely on disseminating academic research.
This repository is a curated bibliography of research papers focusing on Mamba-based State Space Models applied to computer vision tasks. It contains no actual source code, data, or implementations - rather, it serves as a comprehensive survey organizes hundreds of recent arXiv papers by application domain including action recognition, medical imaging, remote sensing, video understanding, and many other CV subfields. The repository's sole content is a regularly updated README.md that lists papers with links to their arXiv versions and associated code repositories when available.
This repository serves as a companion to the survey paper 'Towards Open Vocabulary Learning: A Survey' published in T-PAMI 2024. It contains two markdown files: README.md and dataset.md. The README provides a curated, tabular listing of papers covering open-vocabulary object detection, segmentation, video understanding, and 3D scene understanding, along with related domains. The dataset.md lists standard benchmarks and dataset splits used across these tasks. The repository does not contain any executable code, data processing pipelines, or model implementations. It is purely a literature survey resource. While useful as a reference for researchers, it has no reusable software components and no data lineage to trace.
This repository is a literature survey on recent advances in large language model benchmarks against data contamination, specifically the transition from static to dynamic evaluation. It contains a single README.md file that lists and categorizes numerous research papers, benchmarks, and methods. There is no executable code, data pipelines, notebooks, or any software artifacts. The repository serves as a curated reference list and taxonomy for researchers, but offers no reusable code or data assets. It is not a functional application or a data research project with ETL or model training components.
This repository is a curated 'awesome list' of papers, datasets, and resources for talking head generation, covering image-driven, audio-driven, and NeRF/3D-based approaches. It contains no executable code, data pipelines, or models—only a single README.md with organized tables of references. The repo serves as a research bibliography, not a software project. There are no assets to reproduce or reuse programmatically.