Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.

Last update: Dec 17, 2022

Overview

Multi-Time Attention Networks (mTANs)

This repository contains the PyTorch implementation for the paper Multi-Time Attention Networks for Irregularly Sampled Time Series by Satya Narayan Shukla and Benjamin M. Marlin. This work has been accepted at the International Conference on Learning Representations, 2021.

Requirements

The code requires Python 3.7 or later. The file requirements.txt contains the full list of required Python modules.

pip3 install -r requirements.txt

Training and Evaluation

Interpolation Task on Toy Dataset

python3 tan_interpolation.py --niters 5000 --lr 0.0001 --batch-size 128 --rec-hidden 32 --latent-dim 1 --length 20 --enc mtan_rnn --dec mtan_rnn --n 1000  --gen-hidden 50 --save 1 --k-iwae 5 --std 0.01 --norm --learn-emb --kl --seed 0 --num-ref-points 20 --dataset toy

Interpolation Task on PhysioNet Dataset

python3 tan_interpolation.py --niters 500 --lr 0.001 --batch-size 32 --rec-hidden 64 --latent-dim 16 --quantization 0.016  --enc mtan_rnn --dec mtan_rnn --n 8000  --gen-hidden 50 --save 1 --k-iwae 5 --std 0.01 --norm --learn-emb --kl --seed 0 --num-ref-points 64 --dataset physionet --sample-tp 0.9

Classification Task on PhysioNet Dataset (mTAND-Full)

python3 tan_classification.py --alpha 100 --niters 300 --lr 0.0001 --batch-size 50 --rec-hidden 256 --gen-hidden 50 --latent-dim 20 --enc mtan_rnn --dec mtan_rnn --n 8000 --quantization 0.016 --save 1 --classif --norm --kl --learn-emb --k-iwae 1 --dataset physionet

Classification Task on PhysioNet Dataset (mTAND-Enc)

python3 tanenc_classification.py --niters 200 --lr 0.0001 --batch-size 128 --rec-hidden 128 --enc mtan_enc --n 8000 --quantization 0.016 --save 1 --classif --num-heads 1 --learn-emb --dataset physionet --seed 0

Classification Task on MIMIC-III Dataset (mTAND-Full)

python3 tan_classification.py --alpha 5 --niters 300 --lr 0.0001 --batch-size 128 --rec-hidden 256 --gen-hidden 50 --latent-dim 128 --enc mtan_rnn --dec mtan_rnn   --save 1 --classif --norm --learn-emb --k-iwae 1 --dataset mimiciii

For MIMIC-III Dataset, first you need to have an access to the dataset which can be requested here. We follow the data extraction process described here: https://github.com/mlds-lab/interp-net.

Classification Task on MIMIC-III Dataset (mTAND-Enc)

python3 tanenc_classification.py --niters 200 --lr 0.0001 --batch-size 256 --rec-hidden 256 --enc mtan_enc  --quantization 0.016 --save 1 --classif --num-heads 1 --learn-emb --dataset mimiciii --seed 0

Classification Task on Human Activity Dataset (mTAND-Enc)

python3 tanenc_classification.py --niters 1000 --lr 0.001 --batch-size 256 --rec-hidden 512 --enc mtan_enc_activity  --quantization 0.016 --save 1 --classif --num-heads 1 --learn-emb --dataset activity --seed 0 --classify-pertp

Interpolation Results

Interpolation performance on PhysioNet with varying percent of observed time points:

Classification Results

Classification performance on PhysioNet, MIMIC-III and Human activity dataset, and time per epoch in minutes for all the methods on PhysioNet.

Reference

@inproceedings{
shukla2021multitime,
title={Multi-Time Attention Networks for Irregularly Sampled Time Series},
author={Satya Narayan Shukla and Benjamin Marlin},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=4c0J6lwQ4_}
}

Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.

Related tags

Overview

Multi-Time Attention Networks (mTANs)

Requirements

Training and Evaluation

Interpolation Results

Classification Results

Reference

Owner

The Laboratory for Robust and Efficient Machine Learning

Repo for my Tensorflow/Keras CV experiments. Mostly revolving around the Danbooru20xx dataset

Reference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics

The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.

A PyTorch implementation of PointRend: Image Segmentation as Rendering

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

SE3 Pose Interp - Interpolate camera pose or trajectory in SE3, pose interpolation, trajectory interpolation

The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

Official code of the paper "ReDet: A Rotation-equivariant Detector for Aerial Object Detection" (CVPR 2021)

Deep Learning Training Scripts With Python

Deep Learning Algorithms for Hedging with Frictions

[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Image De-raining Using a Conditional Generative Adversarial Network

Pytorch Implementation for (STANet+ and STANet)

Easy-to-use micro-wrappers for Gym and PettingZoo based RL Environments

Codes for CyGen, the novel generative modeling framework proposed in "On the Generative Utility of Cyclic Conditionals" (NeurIPS-21)

Differentiable Wavetable Synthesis

Display, filter and search log messages in your terminal

SHIFT15M: multiobjective large-scale fashion dataset with distributional shifts