Popular GitHub Repositories for Finite Element Method

?Vite+Vue3+Gin拥有AI辅助的基础开发平台，支持TS和JS混用。它集成了JWT鉴权、权限管理、动态路由、显隐可控组件、分页封装、多点登录拦截、资源权限、上传下载、代码生成器、表单生成器和可配置的导入导出等开发必备功能。

[Tensorflow] High Correlation Data Removing Method for Deep Reinforcement Learning in Obstacle Avoidance and Path Planning

AI 助手全套开源解决方案，自带运营管理后台，开箱即用。集成了 ChatGPT, Azure, ChatGLM,讯飞星火，文心一言等多个平台的大语言模型。支持 MJ AI 绘画，Stable Diffusion AI 绘画，微博热搜等插件工具。采用 Go + Vue3 + element-plus 实现。

Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.

? cool-admin(midway版)一个很酷的后台权限管理框架，Ai编码、流程编排、模块化、插件化、CRUD极速开发，永久开源免费，基于nodejs、typescript、typeorm、mysql、jwt、vue3、vite、element-ui等构建

behaviac is a framework of the game AI development, and it also can be used as a rapid game prototype design tool. behaviac supports the behavior tree, finite state machine and hierarchical task network(BT, FSM, HTN)

Few-shot image translation method working on unstructured environments. ECCV 2022

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

A state-of-the-art semi-supervised method for image recognition

The development repository for the deal.II finite element library

Artificial Intelligence framework for games based on libGDX or not. Features: Steering Behaviors, Formation Motion, Pathfinding, Behavior Trees and Finite State Machines

Danbooru / NovelAI 标签超市

Based on GroundingDino and SAM, use semantic strings to segment any element in an image. The comfyui version of sd-webui-segment-anything.

A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

A simulation framework for RLHF and alternatives. Develop your RLHF method without collecting human data.

A PyTorch implementation of the Deep SVDD anomaly detection method