Maschinelles Lernen · GitHub

Pytorch implementation for "Large-Scale Long-Tailed Recognition in an Open World" (CVPR 2019 ORAL)

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

• Tokyo - NNP - S_LOCATION
• New - NNP - B_LOCATION

Awesome work on object 6 DoF pose estimation

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

A Simple PointPillars PyTorch Implementation for 3D LiDAR(KITTI) Detection.

Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST

深度学习与强化学习的系统学习路线：40 个实验，每个配数学推导笔记、原创插图和从零实现的代码，持续更新中

A practical framework for AI-Assisted Research in Mathematics and Machine Learning

MICA - Towards Metrical Reconstruction of Human Faces [ECCV2022]

Header-Only C++ Library for Graph Representation and Algorithms

A curated collection of papers, code, and resources on backdoor attacks and defenses across AI, from vision and language models to AI agents, generative models, and embodied intelligence.

Fast, scikit-learn-compatible quantile regression forests for prediction intervals and uncertainty estimation.