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Free open-source alternative to TypeForm, TellForm, or Google Forms for building embeddable surveys and forms.
A survey and analysis of various large language model (LLM) agents and their capabilities.
A curated collection of survey papers summarizing advances in deep learning, NLP, CV, and other ML domains.
A curated list of awesome papers about unifying large language models (LLMs) and knowledge graphs (KGs).
A curated list of papers and resources on large language model (LLM) agents, covering methodologies, applications, and challenges.
A curated list of resources on text-to-image generation and synthesis, useful for AI-focused developers.
A curated list of awesome papers and resources for building recommender systems using large language models (LLM).
A curated list of papers that integrate knowledge graphs and large language models for AI and NLP research.
A curated list of practical guide resources for applying Large Language Models in Medicine
An open-source user onboarding platform for creating in-app product tours, checklists, and surveys.
A curated collection of must-read papers and blogs on large language model-based long-context modeling.
A comprehensive paper list and resource repository for Embodied AI research and development.
A comprehensive survey of self-evolving AI agents, bridging foundation models and lifelong agentic systems.
A comprehensive survey of text classification algorithms and techniques in Python.
A comprehensive survey of various question answering (QA) systems, including KBQA, TextQA, TableQA, VisualQA, and MRC.
A survey paper that proposes a taxonomy of Retrieval-Augmented Generation (RAG) techniques for AI-Generated Content (AIGC).
A curated list of Graph/Transformer-based papers and resources for fraud, anomaly, and outlier detection.
A comprehensive collection of research on knowledge graphs, covering various applications and techniques.
A comprehensive collection of data augmentation resources and techniques for developers working with AI tools.
A comprehensive survey of deep learning techniques for 3D point cloud analysis, covering classification, detection, segmentation, and tracking.
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