Research & Education

Thesis is an AI-native platform designed for data science and machine learning, offering an environment where researchers can build and deploy frontier models. The platform allows ML research scientists to run experiments and train models autonomously and at scale within its datacenters. Key features include an intuitive interface for managing datasets, experiments, and models, as well as tools for exploratory data analysis (EDA) and lineage tracking for model development. Thesis aims to accelerate AI R&D, making it easier for data scientists to turn curiosity into consequential discoveries. It offers both a free Spark plan and a 'Pay as you go Ultra' option for production workloads.

Veles is a distributed platform designed for rapid deep learning application development, released under the Apache 2.0 license. It comprises several key components, including the core Veles platform, the Znicz Plugin which serves as a neural network engine, and Mastodon, a bridge facilitating integration between Veles and Java-based systems like Hadoop. Additionally, it features a SoundFeatureExtraction library for audio processing. This platform is ideal for developers and researchers looking to build and deploy deep learning applications in a distributed environment, offering tools for both model development and data processing.

Bert-Multi-Label-Text-Classification offers a PyTorch implementation of pretrained BERT and XLNET models specifically tailored for multi-label text classification. This open-source repository includes a structured codebase with modules for callbacks, configuration, dataset handling, model architecture, output management, text preprocessing, and training. Developers can fine-tune BERT models, preprocess data, and predict new data using provided scripts. The tool supports various dependencies like PyTorch, transformers, and scikit-learn, making it a robust solution for NLP tasks requiring multi-label classification.

TPVFormer is an academic project offering a Tri-Perspective View (TPV) representation for vision-based 3D semantic occupancy prediction, serving as an alternative to Tesla's Occupancy Network for autonomous driving research. It addresses the limitations of traditional bird's-eye-view (BEV) representations by incorporating two additional perpendicular planes, allowing for a more fine-grained description of 3D scenes. The tool features a transformer-based TPV encoder (TPVFormer) to effectively obtain TPV features by aggregating image features. It demonstrates that camera inputs alone can achieve performance comparable to LiDAR-based methods on LiDAR segmentation tasks. The project also includes resources for semantic scene completion and comparisons with Tesla's Occupancy Network.

Tetris-deep-Q-learning-pytorch is an open-source Python project that demonstrates the application of Deep Q-learning for training an AI agent to play the classic game Tetris. Developed with PyTorch, this tool serves as a foundational example of reinforcement learning in action. Users can leverage the provided source code to train their own Tetris-playing models from scratch or test pre-trained models. The project includes all necessary scripts for training and testing, making it accessible for those interested in understanding and experimenting with AI agents and deep learning techniques in a practical gaming context. It's an excellent resource for students and developers exploring the basics of reinforcement learning.

VividTalk is an open-source project designed for one-shot audio-driven talking head generation. It leverages a 3D hybrid prior to produce realistic facial animations directly from audio input. This tool is particularly suitable for researchers and developers working in AI-driven video synthesis and deepfake creation, offering a foundation for exploring advanced animation techniques. As a GitHub repository, it provides the code and resources for users to implement and experiment with the technology, making it a valuable asset for those interested in the technical aspects of generating dynamic talking head videos.

Complexio offers an intelligence layer designed for enterprise AI, connecting an organization's data, people, and systems into a unified operational view. It builds a live map of how work happens, called the Event Knowledge Graph (EKG), providing real-time insights. The Context Broker links this EKG to existing systems and teams, ensuring all insights and actions are grounded in a shared understanding of operational reality. Users can ask questions in natural language through Stevie and receive answers based on their real operations. Additionally, the Automated Automations Engine (AAE) identifies patterns and orchestrates executable workflows, turning observations into automated actions with traceability and control.

WeDLM is an open-source diffusion language model developed by Tencent, designed for high-speed inference. It uniquely reconciles diffusion language models with standard causal attention, enabling native KV cache compatibility with technologies like FlashAttention and PagedAttention. This approach allows for direct initialization from pre-trained autoregressive models such as Qwen2.5 and Qwen3, delivering significant real speedups compared to vLLM-optimized baselines. WeDLM achieves 3-6x speedup on tasks like math reasoning and up to 10x on sequential/counting tasks, while maintaining competitive accuracy. It includes an inference engine, evaluation suite, and a fine-tuning framework, making it a powerful tool for developers and researchers focused on efficient language model deployment.

EduLink AI is dedicated to transforming education with advanced AI solutions, focusing on elevating teaching, learning, and academic integrity. Its core offerings include The Checker AI, designed to safeguard academic integrity by detecting AI-generated content and ensuring the authenticity of student work, and The Tutor AI, an enhanced digital assistant for educators and students that provides AI-powered summaries and tailored lesson plans. EduLink AI's solutions are built for a wide range of educational institutions, from K-12 schools to universities, and are compliant with data privacy regulations like GDPR. The platform also aims to provide inclusive solutions for neurodiversity, adapting to individual learning styles.

aTrain is a powerful GUI tool designed for offline transcription of speech recordings, leveraging state-of-the-art machine learning models for high accuracy and speed. Developed by researchers at the University of Graz, it features speaker diarization to identify different speakers in a recording. A key differentiator is its commitment to privacy, processing all data locally on your device without internet uploads, ensuring GDPR compliance. It supports transcription in 99 languages and offers compatibility with popular qualitative analysis tools like MAXQDA, ATLAS.ti, and nVivo. The tool can run on both CPU and NVIDIA GPUs, with GPU support significantly reducing transcription times.

Lingostar is an AI-powered language learning platform designed to help users improve their speaking skills and build confidence through natural interactions. The platform offers personalized study plans tailored to individual learning needs, focusing on practical conversation practice. Users can engage with an AI conversation partner to practice English, Spanish, and French, receiving feedback to enhance pronunciation and fluency. This tool aims to create an immersive and supportive environment for language acquisition, making it easier for learners to overcome speaking barriers and achieve their language goals.

evidential-deep-learning is an open-source Python package designed to help neural networks learn their own measures of uncertainty directly from data. It provides the necessary code to reproduce the Deep Evidential Regression paper published in NeurIPS 2020, offering a general framework for evidential learning. The tool allows users to integrate evidential layers and loss functions into existing `tf.keras` model pipelines, supporting both fully connected and convolutional layers. This enables the development of models that can provide fast, scalable, and calibrated measures of uncertainty, enhancing their trustworthiness and utility. The package is compatible with Python (>=3.7) and TensorFlow (>=2.0), with PyTorch support planned.

The NVIDIA Data Loading Library (DALI) is a GPU-accelerated library designed to optimize data loading and pre-processing for deep learning applications. It offers a collection of highly optimized building blocks and an efficient execution engine, specifically tailored for processing image, video, and audio data. DALI addresses the common bottleneck of CPU-bound data pipelines by offloading these tasks to the GPU, significantly enhancing performance and scalability for training and inference. It supports various data formats and is portable across popular deep learning frameworks like TensorFlow, PyTorch, and PaddlePaddle. Key features include prefetching, parallel execution, batch processing, and extensibility for custom operators, making it a versatile solution for accelerating complex deep learning workflows.

deepnet is an open-source project providing GPU-based Python implementations of several deep learning algorithms. It supports a range of models including feed-forward neural networks, Restricted Boltzmann Machines, Deep Belief Nets, Autoencoders, Deep Boltzmann Machines, and Convolutional Neural Nets. Built upon the cudamat library by Vlad Mnih and cuda-convnet library by Alex Krizhevsky, deepnet offers a foundational resource for developers and researchers working with deep learning. Its focus on core algorithm implementations makes it a valuable tool for understanding and experimenting with these fundamental AI architectures.

Stenote is an AI-powered meeting transcription and audio recording software designed to transform spoken conversations into accurate, searchable, and actionable notes. It offers instant AI transcripts complete with speaker identification, precise timestamps, and professional summaries, making it an invaluable tool for consultants, coaches, and professional teams. The platform ensures that critical details from meetings are captured and organized, allowing users to focus on discussions without missing important information. Stenote supports multi-platform use, team collaboration, and enterprise-grade security, enhancing productivity and streamlining documentation processes for various professional settings.

HyNote is an AI-powered note-taking and knowledge management product designed to capture, summarize, and organize information from various sources. It supports inputs from meetings, lectures, audio, PDFs, videos, websites, images, and documents. Users can record and transcribe audio in real-time, summarize long articles or documents, and organize notes with features like tagging and folders. Beyond basic note-taking, HyNote enhances learning and productivity by generating flashcards, quizzes, and study plans from notes. It also offers tools for creating podcasts, slides, and infographics, making it a versatile solution for students, professionals, and content creators.

tiny-dnn is a C++14 implementation of deep learning, designed for environments with limited computational resources, such as embedded systems and IoT devices. It stands out as a header-only and dependency-free framework, meaning there's nothing to install beyond a C++14 compiler. This makes it highly portable and easy to integrate into existing applications. The framework supports a variety of network layers, activation functions, loss functions, and optimization algorithms, allowing for the construction of diverse deep learning models. It offers reasonable speed without a GPU, leveraging TBB threading and SSE/AVX vectorization. Additionally, tiny-dnn can import models from Caffe and provides a simple, exception-free operational model, making it a good choice for learning neural networks.

WenetSpeech offers a comprehensive 10000+ hour multi-domain Chinese corpus specifically designed for speech recognition tasks. This extensive dataset is compiled from YouTube and Podcast sources, utilizing both Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) techniques for labeling. To ensure high quality, the corpus undergoes a novel end-to-end label error detection method for validation and filtering. It categorizes data into High Label, Weak Label, and Unlabel sets, suitable for supervised, semi-supervised, or unsupervised training. The dataset also provides various training subsets (S, M, L) and evaluation sets (DEV, TEST_NET, TEST_MEETING) to support diverse ASR system development and benchmarking. Access to the dataset requires visiting the official website, agreeing to the license, and obtaining a password.

AgentBench is a comprehensive benchmark designed to evaluate Large Language Models (LLMs) as agents across a diverse spectrum of environments. It encompasses 8 distinct environments, including 5 newly created domains like Operating System (OS), Database (DB), Knowledge Graph (KG), Digital Card Game (DCG), and Lateral Thinking Puzzles (LTP), alongside 3 recompiled from published datasets (House-Holding, Web Shopping, Web Browsing). The platform offers both Dev and Test splits for each dataset, requiring LLMs to generate responses thousands of times for thorough evaluation. AgentBench also introduces VisualAgentBench for evaluating and training visual foundation agents based on large multimodal models (LMMs), covering embodied, GUI, and visual design environments. It supports quick setup using Docker Compose and provides benchmarking results via a leaderboard.

Vanim is an AI-powered English speaking tutor designed to help users master English with confidence. It offers a 100% free, offline experience with no signup or personal data collection, ensuring privacy. The tool focuses on spoken practice, moving beyond typing and multiple-choice questions, with features like structured learning paths from beginner to advanced, real conversations with AI on various topics, and instant feedback on grammar, vocabulary, pronunciation, and fluency. Users can practice real-world English scenarios, including interviews, office small talk, and casual conversations, making it ideal for job seekers, students, professionals, and travelers.

The bob-plugin-openai-translator is an Open Source macOS plugin designed to enhance text through AI-powered translation, polishing, and grammar correction. Leveraging the OpenAI API, it integrates seamlessly with the Bob application, a macOS platform for translation and OCR. Users can translate text between languages, or polish and correct grammar in the same language by setting the source and target languages identically. This functionality aims to replace tools like Grammarly and supports various languages beyond just English. The plugin also offers a dedicated version, bob-plugin-openai-polisher, for more advanced polishing features, including explanations for modifications. Installation requires Bob (version >= 0.50) and an OpenAI API key.

camel_tools is a comprehensive, open-source Python toolkit developed by the CAMeL Lab at New York University Abu Dhabi, specifically designed for Arabic natural language processing. It offers a wide array of functionalities including text pre-processing, advanced morphological modeling, and specialized components for Dialect Identification, Named Entity Recognition, and Sentiment Analysis. The tool is built to be accessible for researchers and developers, with clear installation instructions for various operating systems like Linux, macOS, and Windows. It also provides options for installing necessary data packages, making it a robust solution for anyone working with the complexities of the Arabic language in NLP tasks.

LLM-Pruner is a cutting-edge tool designed for the structural pruning of large language models (LLMs), as presented at NeurIPS 2023. It enables users to compress LLMs to any desired size while retaining their original multi-task solving abilities. The tool emphasizes task-agnostic compression, requiring minimal training corpus (e.g., 50k Alpaca samples for post-training) and offering efficient compression times, with pruning taking approximately 3 minutes and post-training around 3 hours. LLM-Pruner supports a wide range of popular LLMs, including Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, and TinyLlama. It features an automatic structural pruning process, aiming for minimal human effort, and provides detailed instructions for discovery, estimation, and recovery stages of pruning, along with evaluation using lm-evaluation-harness.

Cron AI specializes in next-generation 3D perception, leveraging cutting-edge deep learning algorithms to process raw data from 3D sensors such as LiDAR. Their flagship senseEDGE platform provides unparalleled accuracy and intelligence in object detection, classification, and tracking, even in challenging environments and adverse weather conditions. It goes beyond traditional methods, offering adaptive flexibility for seamless object detection across varied settings, geographies, and sensor types. The platform is designed for easy deployment at the edge, scaling effortlessly from single-sensor solutions to complex deployments. Cron AI's technology is crucial for intelligent transportation systems, smart spaces, smart security, and automotive applications, ensuring consistent and precise results while being resource-efficient and GDPR compliant.