머신러닝 · GitHub

About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

A curated list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.

An autoML framework & toolkit for machine learning on graphs.

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

A plug-and-play library for parameter-efficient-tuning (Delta Tuning)

프롬프트 학습을 위한 오픈 소스 프레임워크.

• Use the implementations of current prompt-learning approaches.* We have implemented various of prompting methods, including templating, verbalizing and optimization strategies under a unified standard. You can easily call and understand these methods.
• Design your own prompt-learning work. With the extensibility of OpenPrompt, you can quickly practice your prompt-learning ideas.

A list of backdoor learning resources

PyTorch ,ONNX and TensorRT implementation of YOLOv4

Cooking AGI / AGI 大锅烩 · Bilingual (English / 中文) ML & LLM notes with worked examples, step-by-step figures and coding practice. Free to read.

A one-stop library to standardize the inference and evaluation of all the conditional image generation models. [ICLR 2024]

PyTorch Re-Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349

This is the Tensorflow implementation of Convolutional Pose Machines, one of the state-of-the-art models for 2D body and hand pose estimation.

• Easy multi-stage graph construction
• Kalman filters for smooth pose estimation

[ICLR 2025 Spotlight] Official implementation of "Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts"

Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models

Tensorflow framework for the FLAME 3D head model. The code demonstrates how to sample 3D heads from the model, fit the model to 2D or 3D keypoints, and how to generate textured head meshes from Images.