Audio effect modeling with Deep Learning using Pytorch and synthetic dataset generation with Pedalboard

Temporal Convolutions for Multi-Step Quadrotor Motion Prediction

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.

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

Applying Machine Learning Techniques To Assess Whether A Country’s Currency Can Predict The Movement Of Their Respective Stock Market Index

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"

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

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

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

Modelling Spring Reverb with Neural Audio Effects

Multivariate time series analysis on london bike sharing dataset

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

PyTorch implementation of Temporal Convolutional Network

Temporal Convolutional Network for Sequence Modelling

TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation

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

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).

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

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.

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"