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Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression (ICDE 26)
The main features of kbrother/stemdist are: Forecasting Models, Time Series Analysis.
Projects with overlapping indexed features include: rafadd/condtsf — CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting Jianrong Ding, Zhanyu Liu, Guanjie… lyhue1991/eat_tensorflow2_in_30_days — This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow.… adityalab/camul — We require you to have anaconda or miniconda installed. Run the script ./scripts/setup.sh to setup the virtual… adityalab/foil — Dependencies can be installed using the following file: newtimelibenvironment.yml You can obtain the well… adityalab/lstprompt — Implementation of the paper "LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term… adityalab/epifnp — Paper Link: https://arxiv.org/abs/2106.03904.
CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting Jianrong Ding, Zhanyu Liu, Guanjie Zheng†, Haiming Jin, Linghe Kong
This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque
We require you to have anaconda or miniconda installed. Run the script ./scripts/setup.sh to setup the virtual environment with all the required packages.