An implementation of the methods presented in 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
Andrew Jesson
PhD in Machine Learning at University of Oxford @OATML
Andrew Jesson
GitHub Repository
Customer Segmentation using RFM

Customer-Segmentation-using-RFM İş Problemi Bir e-ticaret şirketi müşterilerini segmentlere ayırıp bu segmentlere göre pazarlama stratejileri belirlem

Nazli Sener 7 Dec 26, 2021
Super Resolution for images using deep learning.

Neural Enhance Example #1 — Old Station: view comparison in 24-bit HD, original photo CC-BY-SA @siv-athens. As seen on TV! What if you could increase

Alex J. Champandard 11.7k Dec 29, 2022
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
A library for hidden semi-Markov models with explicit durations

hsmmlearn hsmmlearn is a library for unsupervised learning of hidden semi-Markov models with explicit durations. It is a port of the hsmm package for

Joris Vankerschaver 69 Dec 20, 2022
Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)

Open Compound Domain Adaptation [Project] [Paper] [Demo] [Blog] Overview Open Compound Domain Adaptation (OCDA) is the author's re-implementation of t

Zhongqi Miao 137 Dec 15, 2022
How to Predict Stock Prices Easily Demo

How-to-Predict-Stock-Prices-Easily-Demo How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube ##Overview This is th

Siraj Raval 752 Nov 16, 2022
Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets"

Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Data

2 Oct 06, 2022
An all-in-one application to visualize multiple different local path planning algorithms

Table of Contents Table of Contents Local Planner Visualization Project (LPVP) Features Installation/Usage Local Planners Probabilistic Roadmap (PRM)

Abdur Javaid 47 Dec 30, 2022
This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti

Albert Webson 64 Dec 11, 2022
Code and models used in "MUSS Multilingual Unsupervised Sentence Simplification by Mining Paraphrases".

Multilingual Unsupervised Sentence Simplification Code and pretrained models to reproduce experiments in "MUSS: Multilingual Unsupervised Sentence Sim

Facebook Research 81 Dec 29, 2022
This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization This is the code for our paper ``SumGNN: Multi-typed Drug

Yue Yu 58 Dec 21, 2022
[ICCV-2021] An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation

An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation (ICCV 2021) Introduction This is an official pytorch implemen

rongchangxie 42 Jan 04, 2023
Can we do Customers Segmentation using PHP and Unsupervized Machine Learning ? Yes we can ! 🤡

Customers Segmentation using PHP and Rubix ML PHP Library Can we do Customers Segmentation using PHP and Unsupervized Machine Learning ? Yes we can !

Mickaël Andrieu 11 Oct 08, 2022
A collection of resources and papers on Diffusion Models, a darkhorse in the field of Generative Models

This repository contains a collection of resources and papers on Diffusion Models and Score-based Models. If there are any missing valuable resources

5.1k Jan 08, 2023
A python library to artfully visualize Factorio Blueprints and an interactive web demo for using it.

Factorio Blueprint Visualizer I love the game Factorio and I really like the look of factories after growing for many hours or blueprints after tweaki

Piet Brömmel 124 Jan 07, 2023
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

Thomas Winters 36 Nov 15, 2022
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-dri

Neural Magic 1.5k Dec 30, 2022
Source code for CVPR2022 paper "Abandoning the Bayer-Filter to See in the Dark"

Abandoning the Bayer-Filter to See in the Dark (CVPR 2022) Paper: https://arxiv.org/abs/2203.04042 (Arxiv version) This code includes the training and

74 Dec 15, 2022
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

PEPit: Performance Estimation in Python This open source Python library provides a generic way to use PEP framework in Python. Performance estimation

Baptiste 53 Nov 16, 2022
Extract MNIST handwritten digits dataset binary file into bmp images

MNIST-dataset-extractor Extract MNIST handwritten digits dataset binary file into bmp images More info at http://yann.lecun.com/exdb/mnist/ Dependenci

Omar Mostafa 6 May 24, 2021