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A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
A collection of important graph embedding, classification and representation learning papers with implementations.
[ICLR 2023] "Dilated convolution with learnable spacings" Ismail Khalfaoui Hassani, Thomas Pellegrini and Timothée Masquelier
PyTorch implementation of some attentions for Deep Learning Researchers.
Pytorch Implementation of Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
Reproducing the paper: "Time2Vec: Learning a Vector Representation of Time" - https://arxiv.org/pdf/1907.05321.pdf
Early stopping for PyTorch
Tensorflow implementation of Amazon DeepAR
Benchmark datasets, data loaders, and evaluators for graph machine learning
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
Dual Staged Attention Model for Time Series prediction
Three (dis)similarity measures for time series implemented in Tensorflow
The best solution of the Weather Prediction track in the Yandex Shifts challenge
Ensemble feature ranking for SuperLearner variable selection
A Tensorflow 2 (Keras) implementation of DA-RNN (A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction, arXiv:1704.02971)
Attention mechanism for processing sequential data that considers the context for each timestamp.
CapsLayer: An advanced library for capsule theory
A Tensorflow implementation of CapsNet(Capsules Net) in paper Dynamic Routing Between Capsules
Keras implementation of Non-local Neural Networks
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
Time Series Feature Extraction using Deep Learning
This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method
An intuitive library to extract features from time series.
Google Research