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

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)

Distributed deep learning on Hadoop and Spark clusters.

Falken provides developers with a service that allows them to train AI that can play their games

Implementation of K-Nearest Neighbors Algorithm Using PySpark

The project's goal is to show a real world application of image segmentation using k means algorithm

Learning --> Numpy January 2022 - winter'22

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

Apple-voice-recognition - Machine Learning

Primitives for machine learning and data science.

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning

Simple data balancing baselines for worst-group-accuracy benchmarks.

Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)

This repository contains full machine learning pipeline of the Zillow Houses competition on Kaggle platform.

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

pandas, scikit-learn, xgboost and seaborn integration

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning

Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

flexible time-series processing & feature extraction

Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations.

LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRerank, Seq2Slate.