This repo contains official implementations of **Coach AI Badminton Project** from Advanced Database System Laboratory, National Yang Ming Chiao Tung University supervised by Prof. Wen-Chih Peng. The high-level concepts of each project are as follows: 1. Visualization Platform published at _Physical Education Journal 2020_ aims to construct a platform that can be used to illustrate the data from matches. 2. Shot Influence and Extension Work published at _ICDM-21_ and _ACM TIST 2022_ , respectively introduce a framework with a shot encoder, a pattern extractor, and a rally encoder to capture long short-term dependencies for evaluating players' performance of each shot. 3. Stroke Forecasting published at _AAAI-22_ proposes the first stroke forecasting task to predict the future strokes of both players based on the given strokes by ShuttleNet, a position-aware fusion of rally progress and player styles framework. 4. Strategic Environment published at _AAAI-23 Student Abstract_ designs a safe and reproducible badminton environment for turn-based sports, which simulates rallies with different angles of view and designs the states, actions, and training procedures. 5. Movement Forecasting published at _AAAI-23_ proposes the first movement forecasting task, which contains not only the goal of stroke forecasting but also the movement of players, by DyMF, a novel dynamic graphs and hierarchical fusion model based on the proposed player movements (PM) graphs. 6. CoachAI-Challenge-IJCAI2023 is a badminton challenge (CC4) hosted at _IJCAI-23_. Please find the website for more details. 7. ShuttleSet published at _KDD-23_ is the largest badminton singles dataset with stroke-level records. - An extension dataset ShuttleSet22 published at _IJCAI-24 Demo & IJCAI-23 IT4PSS Workshop_ is also released. 8. CoachAI Badminton Environment published at _AAAI-24 Student Abstract and Demo, DSAI4Sports @ KDD 2023_ is a reinforcement learning (RL) environment tailored for AI-driven sports analytics, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Video GigaGAN - Pytorch is an implementation of Video GigaGAN, a state-of-the-art video upsampling technique developed by Adobe AI labs. The project aims to provide a Pytorch implementation for researchers and developers interested in video super-resolution. The codebase allows users to replicate the results of the original research paper and experiment with video upscaling techniques. The repository includes the necessary code and resources to train and test the GigaGAN model on video datasets. Researchers can leverage this implementation to enhance the visual quality of low-resolution videos and explore advancements in video super-resolution technology.

This repository, RAM, focuses on developing advanced algorithms and methods for Reasoning, Alignment, Memory. It contains projects related to these areas and is maintained by a team of individuals. The repository is licensed under the MIT License.

This repository is an awesome list dedicated to resources for using Large Language Models (LLMs) to generate JSON or other structured outputs. It includes terminology explanations, hosted and local models, Python libraries, blog articles, videos, Jupyter notebooks, and leaderboards related to LLMs and JSON generation. The repository covers various aspects such as function calling, JSON mode, guided generation, and tool usage with different providers and models.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

Agents 2.0 is a framework for training language agents using symbolic learning, inspired by connectionist learning for neural nets. It implements main components of connectionist learning like back-propagation and gradient-based weight update in the context of agent training using language-based loss, gradients, and weights. The framework supports optimizing multi-agent systems and allows multiple agents to take actions in one node.

Implementation of Double DQN reinforcement learning for OpenAI Gym environments with discrete action spaces. The algorithm aims to improve sample efficiency by using two uncorrelated Q-Networks to prevent overestimation of Q-values. By updating parameters periodically, the model reduces computation time and enhances training performance. The tool is based on the Double DQN method proposed by Hasselt in 2010.

Implementation of the general framework for AMIE, from the paper Towards Conversational Diagnostic AI, out of Google Deepmind. This repository provides a Pytorch implementation of the AMIE framework, aimed at enabling conversational diagnostic AI. It is a work in progress and welcomes collaboration from individuals with a background in deep learning and an interest in medical applications.

WeatherGFT is a physics-AI hybrid model designed to generalize weather forecasts to finer-grained temporal scales beyond the training dataset. It incorporates physical partial differential equations (PDEs) into neural networks to simulate fine-grained physical evolution and correct biases. The model achieves state-of-the-art performance in forecasting tasks at different time scales, from nowcasting to medium-range forecasts, by utilizing a lead time-aware training framework and a carefully designed PDE kernel. WeatherGFT bridges the gap between nowcast and medium-range forecast by extending forecasting abilities to predict accurately at a 30-minute time scale.

AITom is an open-source platform for AI-driven cellular electron cryo-tomography analysis. It is developed to process large amounts of Cryo-ET data, reconstruct, detect, classify, recover, and spatially model different cellular components using state-of-the-art machine learning approaches. The platform aims to automate cellular structure discovery and provide new insights into molecular biology and medical applications.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

This repository showcases the author's experiments with Large Language Models (LLMs) for text generation tasks. It includes dataset preparation, preprocessing, model fine-tuning using libraries such as Axolotl and HuggingFace, and model evaluation.

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.

Mooncake is a serving platform for Kimi, a leading LLM service provided by Moonshot AI. It features a KVCache-centric disaggregated architecture that separates prefill and decoding clusters, leveraging underutilized CPU, DRAM, and SSD resources of the GPU cluster. Mooncake's scheduler balances throughput and latency-related SLOs, with a prediction-based early rejection policy for highly overloaded scenarios. It excels in long-context scenarios, achieving up to a 525% increase in throughput while handling 75% more requests under real workloads.

This repo contains official implementations of **Coach AI Badminton Project** from Advanced Database System Laboratory, National Yang Ming Chiao Tung University supervised by Prof. Wen-Chih Peng. The high-level concepts of each project are as follows: 1. Visualization Platform published at _Physical Education Journal 2020_ aims to construct a platform that can be used to illustrate the data from matches. 2. Shot Influence and Extension Work published at _ICDM-21_ and _ACM TIST 2022_ , respectively introduce a framework with a shot encoder, a pattern extractor, and a rally encoder to capture long short-term dependencies for evaluating players' performance of each shot. 3. Stroke Forecasting published at _AAAI-22_ proposes the first stroke forecasting task to predict the future strokes of both players based on the given strokes by ShuttleNet, a position-aware fusion of rally progress and player styles framework. 4. Strategic Environment published at _AAAI-23 Student Abstract_ designs a safe and reproducible badminton environment for turn-based sports, which simulates rallies with different angles of view and designs the states, actions, and training procedures. 5. Movement Forecasting published at _AAAI-23_ proposes the first movement forecasting task, which contains not only the goal of stroke forecasting but also the movement of players, by DyMF, a novel dynamic graphs and hierarchical fusion model based on the proposed player movements (PM) graphs. 6. CoachAI-Challenge-IJCAI2023 is a badminton challenge (CC4) hosted at _IJCAI-23_. Please find the website for more details. 7. ShuttleSet published at _KDD-23_ is the largest badminton singles dataset with stroke-level records. - An extension dataset ShuttleSet22 published at _IJCAI-24 Demo & IJCAI-23 IT4PSS Workshop_ is also released. 8. CoachAI Badminton Environment published at _AAAI-24 Student Abstract and Demo, DSAI4Sports @ KDD 2023_ is a reinforcement learning (RL) environment tailored for AI-driven sports analytics, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.