Attention temporal convolutional network for EEG-based motor imagery classification

A Keras library for multi-step time-series forecasting.

Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos

PyTorch implementation of Densely Connected Time Delay Neural Network

Time Series Analysis with Python Cookbook, Second Edition - Published by Packt

TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

Modelling Spring Reverb with Neural Audio Effects

A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in rs-fMRI Data

The repository has the PyTorch codes to reproduce the results for our recently accepted paper, "Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network", in SEG Technical Program Expanded Abstracts, 2019.

self-supervised learning, deep learning, representation learning, RotNet, temporal convolutional network(TCN), deformation transformation, sketch pre-train, sketch classification, sketch retrieval, free-hand sketch, official code of paper "Deep Self-Supervised Representation Learning for Free-Hand Sketch"

[IEEE J-BHI-2024] The PyTorch implementation of MASA-TCN

This repo contains an implementation code for the weakly supervised surgical tool tracker. In this research, the temporal dependency in surgical video data is modeled using a convolutional LSTM which is trained only on image level labels to detect, localize and track surgical instruments.

APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily

Temporal Convolutional Network for Sequence Modelling

ETH Zürich Semester Project exploring the possibilities of using Deep Learning to extract vital signs from Radar Data

Source code (train/test) accompanying the paper entitled "Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach" in CVPR 2019 (https://arxiv.org/abs/1903.10764).

Undergraduate group project in which we built an ECG classifier using a TCN-CNN with 97% accuracy

Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network

Official Implementation of Integrating Physics-Informed Vectors for Improved Wind Speed Forecasting with Neural Networks

PyTorch implementation of Temporal Convolutional Network

Action Classification on Breakfast Dataset using Temporal Convolutional Networks (TCN)

Protein secondary structure prediction from amino acid sequence using machine learning

comprehensive collection of powerful techniques for time series data visualization, analysis and modeling

RUL prediction of engines from CMAPSS Data

Build end-to-end Deep Neural Network to translate speech to text (ASR model)

"Official code for TCRAT-Pred: Multi-Agents Trajectory Prediction for Autonomous Vehicles with Multi-Modal Predictions (IEEE CogInfoCom 2024)"

Multivariate time series analysis on london bike sharing dataset

Temporal Convolutions for Multi-Step Quadrotor Motion Prediction

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This repository contains code and resources for a project focused on predicting traffic volume using Temporal Convolutional Networks (TCNs). Leveraging the Metro Interstate Traffic Volume dataset from 2012-2018, the project aims to develop an accurate model for short- to medium-term traffic volume forecasting in Minneapolis-St Paul, MN.