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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
A complete computer science study plan to become a software engineer.
Source codes for the paper "Diff-MTS: Temporal-Augmented Conditional Diffusion-Based AIGC for Industrial Time Series Toward the Large Model Era"
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Code implementation for ImbalancedLearningRL, a reinforcement learning-based training method for imbalanced (outcome label) training data.
Imbalanced Classification with Deep Reinforcement Learning
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
提供同花顺客户端/国金/华泰客户端/雪球的基金、股票自动程序化交易以及自动打新，支持跟踪 joinquant /ricequant 模拟交易 和 实盘雪球组合, 量化交易组件
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas…
DC3: A Learning Method for Optimization with Hard Constraints
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Official implementation of "Multi-Task Learning as a Bargaining Game" [ICML 2022]
PyTorch implementation of "Towards Impartial Multi-Task Learning"
A PyTorch Library for Multi-Task Learning
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
simple demo codes for Learning to Teach with Dynamic Loss Functions
[NeurIPS2022 Cellseg] MT2: Multi-task Mean Teacher for Semi-Supervised Cell Segmentation
OptNet: Differentiable Optimization as a Layer in Neural Networks
Virtual Adversarial Training (VAT) implementation for PyTorch
A state-of-the-art semi-supervised method for image recognition
A PyTorch-based library for semi-supervised learning (NeurIPS'21)
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Sample files to accompany the FT's Chart Doctor column