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

This is the code repository for Interpretable Machine Learning with Python, published by Packt.

Continuously evaluated, functional, incremental, time-series forecasting

Anomaly Detection and Correlation library

SIMD-accelerated bitwise hamming distance Python module for hexidecimal strings

CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

flexible time-series processing & feature extraction

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Firebase + Cloudrun + Machine learning

Titanic Traveller Survivability Prediction

Pydantic based mock data generation

A scikit-learn based module for multi-label et. al. classification

icepickle is to allow a safe way to serialize and deserialize linear scikit-learn models

Code for the TCAV ML interpretability project

Machine Learning for Time-Series with Python.Published by Packt

The Simpsons and Machine Learning: What makes an Episode Great?

Library for machine learning stacking generalization.

TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

A machine learning project that predicts the price of used cars in the UK

We have a dataset of user performances. The project is to develop a machine learning model that will predict the salaries of baseball players.

A collection of interactive machine-learning experiments: 🏋️models training + 🎨models demo