Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

Related tags

Deep Learningcausal-bald
Overview

causal-bald

| Abstract | Installation | Example | Citation | Reproducing Results DUE

An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

Evolution of CATE function with Causal BALD acquisition strategy

Abstract

Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical or expensive. Existing approaches rely on fitting deep models on outcomes observed for treated and control populations, but when measuring the outcome for an individual is costly (e.g. biopsy) a sample efficient strategy for acquiring outcomes is required. Deep Bayesian active learning provides a framework for efficient data acquisition by selecting points with high uncertainty. However, naive application of existing methods selects training data that is biased toward regions where the treatment effect cannot be identified because there is non-overlapping support between the treated and control populations. To maximize sample efficiency for learning personalized treatment effects, we introduce new acquisition functions grounded in information theory that bias data acquisition towards regions where overlap is satisfied, by combining insights from deep Bayesian active learning and causal inference. We demonstrate the performance of the proposed acquisition strategies on synthetic and semi-synthetic datasets IHDP and CMNIST and their extensions which aim to simulate common dataset biases and pathologies.

Installation

$ git clone [email protected]:[anon]/causal-bald.git
$ cd causal-bald
$ conda env create -f environment.yml
$ conda activate causal-bald

[Optional] For developer mode

$ pip install -e .

Example

Active learning loop

First run using random acquisition:

causal-bald \
    active-learning \
        --job-dir experiments/ \
        --num-trials 5 \
        --step-size 10 \
        --warm-start-size 100 \
        --max-acquisitions 38 \
        --acquisition-function random \
        --temperature 0.25 \
        --gpu-per-trial 0.2 \
    ihdp \
        --root assets/ \
    deep-kernel-gp

Now run using $\mu\rho\textrm{-BALD}$ acquisition.

causal-bald \
    active-learning \
        --job-dir experiments/ \
        --num-trials 5 \
        --step-size 10 \
        --warm-start-size 100 \
        --max-acquisitions 38 \
        --acquisition-function mu-rho \
        --temperature 0.25 \
        --gpu-per-trial 0.2 \
    ihdp \
        --root assets/ \
    deep-kernel-gp

Evaluation

Evaluate PEHE at each acquisition step

causal-bald \
    evaluate \
        --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-random_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ \
        --output-dir experiments/due/ihdp \
    pehe
causal-bald \
    evaluate \
        --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-mu-rho_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ \
        --output-dir experiments/due/ihdp \
    pehe

Plot results

causal-bald \
    evaluate \
        --experiment-dir experiments/due/ihdp \
    plot-convergence \
        -m mu-rho \
        -m random

Plotting convergence of acquisitions. Comparing random and mu-rho for example code

Citation

If you find this code helpful for your work, please cite our paper Paper as

@article{jesson2021causal,
  title={Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data},
  author={Jesson, Andrew and Tigas, Panagiotis and van Amersfoort, Joost and Kirsch, Andreas and Shalit, Uri and Gal, Yarin},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  year={2021}
}

Reprodcuing Results Due

IHDP

$\mu\rho$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function mu-rho --temperature 0.25 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-mu-rho_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

$\mu$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function mu --temperature 0.25 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-mu_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

$\mu\pi$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function mu-pi --temperature 0.25 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-mu-pi_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

$\rho$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function rho --temperature 0.25 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-rho_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

$\pi$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function pi --temperature 0.25 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-pi_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

$\tau$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function tau --temperature 0.25 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-tau_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

Random

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function random --temperature 0.25 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-random_temp-0.25/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

Sundin

causal-bald active-learning --job-dir experiments/ --num-trials 200 --step-size 10 --warm-start-size 100 --max-acquisitions 38 --acquisition-function sundin --temperature 1.0 --gpu-per-trial 0.2 ihdp --root assets/ deep-kernel-gp
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-100_ma-38_af-sundin_temp-1.0/ihdp/deep_kernel_gp/kernel-Matern32_ip-100-dh-200_do-1_dp-3_ns--1.0_dr-0.1_sn-0.95_lr-0.001_bs-100_ep-500/ --output-dir experiments/due/ihdp pehe

Plot Results

causal-bald \
    evaluate \
        --experiment-dir experiments/due/ihdp \
    plot-convergence \
        -m mu-rho \
        -m mu \
        -m mu-pi \
        -m rho \ \
        -m pi
        -m tau \
        -m random \
        -m sundin

Synthetic

Synthetic dataset

Synthetic: $\mu\rho$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function mu-rho --temperature 0.25 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-mu-rho_temp-0.25/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/synthetic pehe

Synthetic: $\mu$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function mu --temperature 0.25 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-mu_temp-0.25/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/ihdp pehe

Synthetic: $\mu\pi$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function mu-pi --temperature 0.25 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-mu-pi_temp-0.25/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/synthetic pehe

Synthetic: $\rho$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function rho --temperature 0.25 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-rho_temp-0.25/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/synthetic pehe

Synthetic: $\pi$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function pi --temperature 0.25 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-pi_temp-0.25/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/synthetic pehe

Synthetic: $\tau$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function tau --temperature 0.25 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-tau_temp-0.25/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/synthetic pehe

Synthetic: Random

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function random --temperature 0.25 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-random_temp-0.25/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/synthetic pehe

Synthetic: Sundin

causal-bald active-learning --job-dir experiments/ --num-trials 40 --step-size 10 --warm-start-size 10 --max-acquisitions 31 --acquisition-function sundin --temperature 1.0 --gpu-per-trial 0.2 synthetic deep-kernel-gp --kernel RBF --dim-hidden 100 --num-inducing-points 20 --negative-slope 0.0 --batch-size 200 --dropout-rate 0.2
causal-bald evaluate --experiment-dir experiments/active_learning/ss-10_ws-10_ma-31_af-sundin_temp-1.0/synthetic/deep_kernel_gp/kernel-RBF_ip-20-dh-100_do-1_dp-3_ns-0.0_dr-0.2_sn-0.95_lr-0.001_bs-200_ep-500/ --output-dir experiments/due/synthetic pehe

Synthetic: Plot Results

causal-bald \
    evaluate \
        --experiment-dir experiments/due/synthetic \
    plot-convergence \
        -m mu-rho \
        -m mu \
        -m mu-pi \
        -m rho \ \
        -m pi
        -m tau \
        -m random \
        -m sundin

CMNIST

CMNIST dataset

CMNIST: $\mu\rho$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function mu-rho --temperature 0.25 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-mu-rho_temp-0.25/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/cmnist pehe

CMNIST: $\mu$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function mu --temperature 0.25 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-mu_temp-0.25/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/ihdp pehe

CMNIST: $\mu\pi$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function mu-pi --temperature 0.25 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-mu-pi_temp-0.25/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/cmnist pehe

CMNIST: $\rho$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function rho --temperature 0.25 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-rho_temp-0.25/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/cmnist pehe

CMNIST: $\pi$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function pi --temperature 0.25 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-pi_temp-0.25/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/cmnist pehe

CMNIST: $\tau$-BALD

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function tau --temperature 0.25 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-tau_temp-0.25/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/cmnist pehe

CMNIST: Random

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function random --temperature 0.25 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-random_temp-0.25/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/cmnist pehe

CMNIST: Sundin

causal-bald active-learning --job-dir experiments/ --num-trials 10 --step-size 50 --warm-start-size 250 --max-acquisitions 56 --acquisition-function sundin --temperature 1.0 --gpu-per-trial 0.5 cmnist --root assets/ deep-kernel-gp --kernel RBF --depth 2 --dropout-rate 0.05 --spectral-norm 3.0 --batch-size 64
causal-bald evaluate --experiment-dir experiments/active_learning/ss-50_ws-250_ma-56_af-sundin_temp-1.0/cmnist/deep_kernel_gp/kernel-RBF_ip-100-dh-200_do-1_dp-2_ns--1.0_dr-0.05_sn-3.0_lr-0.001_bs-64_ep-500/ --output-dir experiments/due/cmnist pehe

CMNIST: Plot Results

causal-bald \
    evaluate \
        --experiment-dir experiments/due/cmnist \
    plot-convergence \
        -m mu-rho \
        -m mu \
        -m mu-pi \
        -m rho \ \
        -m pi
        -m tau \
        -m random \
        -m sundin
Owner
OATML
Oxford Applied and Theoretical Machine Learning Group
OATML
GitHub Repository
A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset. This repo contains scripts to train RL agents to navigate the closed world and collect vi

MUGEN 11 Oct 22, 2022
Pacman-AI - AI project designed by UC Berkeley. Designed reflex and minimax agents for the game Pacman.

Pacman AI Jussi Doherty CAP 4601 - Introduction to Artificial Intelligence - Fall 2020 Python version 3.0+ Source of this project This repo contains a

Jussi Doherty 1 Jan 03, 2022
A list of multi-task learning papers and projects.

This page contains a list of papers on multi-task learning for computer vision. Please create a pull request if you wish to add anything. If you are interested, consider reading our recent survey pap

svandenh 297 Dec 17, 2022
A framework for joint super-resolution and image synthesis, without requiring real training data

SynthSR This repository contains code to train a Convolutional Neural Network (CNN) for Super-resolution (SR), or joint SR and data synthesis. The met

83 Jan 01, 2023
Implementation of the state of the art beat-detection, downbeat-detection and tempo-estimation model

The ISMIR 2020 Beat Detection, Downbeat Detection and Tempo Estimation Model Implementation. This is an implementation in TensorFlow to implement the

Koen van den Brink 1 Nov 12, 2021
PyTorch implementation for View-Guided Point Cloud Completion

PyTorch implementation for View-Guided Point Cloud Completion

22 Jan 04, 2023
给yolov5加个gui界面，使用pyqt5，yolov5是5.0版本

博文地址 https://xugaoxiang.com/2021/06/30/yolov5-pyqt5 代码执行 项目中使用YOLOv5的v5.0版本，界面文件是project.ui pip install -r requirements.txt python main.py 图片检测 视频检测

Xu GaoXiang 215 Dec 30, 2022
Reproduction process of AlexNet

PaddlePaddle论文复现杂谈 背景 注：该repo基于PaddlePaddle，对AlexNet进行复现。时间仓促，难免有所疏漏，如果问题或者想法，欢迎随时提issue一块交流。 飞桨论文复现赛地址：https://aistudio.baidu.com/aistudio/competitio

19 Nov 29, 2022
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

News December 27: v1.1.0 New loss functions: CentroidTripletLoss and VICRegLoss Mean reciprocal rank + per-class accuracies See the release notes Than

Kevin Musgrave 5k Jan 05, 2023
A template repository for submitting a job to the Slurm Cluster installed at the DISI - University of Bologna

Cluster di HPC con GPU per esperimenti di calcolo (draft version 1.0) Per poter utilizzare il cluster il primo passo è abilitare l'account istituziona

20 Dec 16, 2022
This is the code of using DQN to play Sekiro .

Update for using DQN to play sekiro 2021.2.2（English Version） This is the code of using DQN to play Sekiro . I am very glad to tell that I have writen

144 Dec 25, 2022
A simple image/video to Desmos graph converter run locally

Desmos Bezier Renderer A simple image/video to Desmos graph converter run locally Sample Result Setup Install dependencies apt update apt install git

Kevin JY Cui 339 Dec 23, 2022
Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Google Cloud Platform 792 Dec 28, 2022
Churn-Prediction-Project - In this project, a churn prediction model is developed for a private bank as a term project for Data Mining class.

Churn-Prediction-Project In this project, a churn prediction model is developed for a private bank as a term project for Data Mining class. Project in

1 Jan 03, 2022
Learning to Predict Gradients for Semi-Supervised Continual Learning

Learning to Predict Gradients for Semi-Supervised Continual Learning Code for project: "Learning to Predict Gradients for Semi-Supervised Continual Le

Yan Luo 2 Mar 05, 2022
SNIPS: Solving Noisy Inverse Problems Stochastically

SNIPS: Solving Noisy Inverse Problems Stochastically This repo contains the official implementation for the paper SNIPS: Solving Noisy Inverse Problem

Bahjat Kawar 35 Nov 09, 2022
Implementation of SSMF: Shifting Seasonal Matrix Factorization

SSMF Implementation of SSMF: Shifting Seasonal Matrix Factorization, Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. NeurIPS, 2021

Koki Kawabata 9 Jun 10, 2022
Training and Evaluation Code for Neural Volumes

Neural Volumes This repository contains training and evaluation code for the paper Neural Volumes. The method learns a 3D volumetric representation of

Meta Research 370 Dec 08, 2022
[CVPR 2021] Exemplar-Based Open-Set Panoptic Segmentation Network (EOPSN)

EOPSN: Exemplar-Based Open-Set Panoptic Segmentation Network (CVPR 2021) PyTorch implementation for EOPSN. We propose open-set panoptic segmentation t

Jaedong Hwang 49 Dec 30, 2022
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

CO-PILOT CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum, NeurIPS 2021, Shuang Ao, Tianyi Zhou, Guodong Long, Qingh

Shuang Ao 1 Feb 18, 2022