Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Last update: Dec 06, 2022

Related tags

Overview

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Introduction.

This package includes the python codes for implementing Temporal Alignment Prediction (TAP) in supervised learning and few-shot action recognition, described in

Bing Su and Ji-Rong Wen. "Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification" ICLR, 2022.

License & disclaimer.

The codes can be used for research purposes only. This package is strictly for non-commercial academic use only.
Usage

The "Supervised_Learning" folder contains codes for using TAP in supervised distance learning for sequence data.

The "Few_Shot_Learning" folder contains codes for using TAP in few-shot action recognition.

Please refer to the ReadMe files in the folds.
Citations

Please cite the following paper if you use the codes:

Bing Su and Ji-Rong Wen, "Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification", ICLR, 2022.

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Related tags

Overview

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Owner

A Python implementation of GRAIL, a generic framework to learn compact time series representations.

Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Machine Learning University: Accelerated Natural Language Processing Class

Greykite: A flexible, intuitive and fast forecasting library

My project contrasts K-Nearest Neighbors and Random Forrest Regressors on Real World data

Binary Classification Problem with Machine Learning

healthy and lesion models for learning based on the joint estimation of stochasticity and volatility

Drug prediction

Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational model)

2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions.

Machine Learning e Data Science com Python

InfiniteBoost: building infinite ensembles with gradient descent

Python-based implementations of algorithms for learning on imbalanced data.

A high-performance topological machine learning toolbox in Python

A Time Series Library for Apache Spark

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.

High performance Python GLMs with all the features!

Apache (Py)Spark type annotations (stub files).

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Related tags

Overview

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Owner

A Python implementation of GRAIL, a generic framework to learn compact time series representations.

Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Machine Learning University: Accelerated Natural Language Processing Class

﻿Greykite: A flexible, intuitive and fast forecasting library

My project contrasts K-Nearest Neighbors and Random Forrest Regressors on Real World data

Binary Classification Problem with Machine Learning

healthy and lesion models for learning based on the joint estimation of stochasticity and volatility

Drug prediction

Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational model)

2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions.

Machine Learning e Data Science com Python

InfiniteBoost: building infinite ensembles with gradient descent

Python-based implementations of algorithms for learning on imbalanced data.

A high-performance topological machine learning toolbox in Python

A Time Series Library for Apache Spark

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.

High performance Python GLMs with all the features!

Apache (Py)Spark type annotations (stub files).

Greykite: A flexible, intuitive and fast forecasting library