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
PyTorch implementation for STIN

STIN This repository contains PyTorch implementation for STIN. Abstract: In single-photon LiDAR, photon-efficient imaging captures the 3D structure of

Yiweins 2 Nov 22, 2022
Kaggle-titanic - A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Kaggle-titanic This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this reposito

Andrew Conti 800 Dec 15, 2022
Torchyolo - Yolov3 ve Yolov4 modellerin Pytorch uygulamasıdır

TORCHYOLO : Yolo Modellerin Pytorch Uygulaması Yapılacaklar: Yolov3 model.py ve

Kadir Nar 3 Aug 22, 2022
Scripts and misc. stuff related to the PortSwigger Web Academy

PortSwigger Web Academy Notes Mostly scripts to automate the exploits. Going in the order of the recomended learning path - starting with SQLi. Commun

pageinsec 17 Dec 30, 2022
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

MeTAL - Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral) Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaes

Sungyong Baik 44 Dec 29, 2022
This is the official code of L2G, Unrolling and Recurrent Unrolling in Learning to Learn Graph Topologies.

Learning to Learn Graph Topologies This is the official code of L2G, Unrolling and Recurrent Unrolling in Learning to Learn Graph Topologies. Requirem

Stacy X PU 16 Dec 09, 2022
Lava-DL, but with PyTorch-Lightning flavour

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Sami BARCHID 4 Oct 31, 2022
CLASP - Contrastive Language-Aminoacid Sequence Pretraining

CLASP - Contrastive Language-Aminoacid Sequence Pretraining Repository for creating models pretrained on language and aminoacid sequences similar to C

Michael Pieler 133 Dec 29, 2022
My implementation of Image Inpainting - A deep learning Inpainting model

Image Inpainting What is Image Inpainting Image inpainting is a restorative process that allows for the fixing or removal of unwanted parts within ima

Joshua V Evans 1 Dec 12, 2021
ALBERT-pytorch-implementation - ALBERT pytorch implementation

ALBERT-pytorch-implementation developing... 모델의 개념이해를 돕기 위한 구현물로 현재 변수명을 상세히 적었고

BG Kim 3 Oct 06, 2022
A library for optimization on Riemannian manifolds

TensorFlow RiemOpt A library for manifold-constrained optimization in TensorFlow. Installation To install the latest development version from GitHub:

Oleg Smirnov 83 Dec 27, 2022
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

TANG, shixiang 6 Nov 25, 2022
A Marvelous ChatBot implement using PyTorch.

PyTorch Marvelous ChatBot [Update] it's 2019 now, previously model can not catch up state-of-art now. So we just move towards the future a transformer

JinTian 223 Oct 18, 2022
FindFunc is an IDA PRO plugin to find code functions that contain a certain assembly or byte pattern, reference a certain name or string, or conform to various other constraints.

FindFunc: Advanced Filtering/Finding of Functions in IDA Pro FindFunc is an IDA Pro plugin to find code functions that contain a certain assembly or b

213 Dec 17, 2022
Official PyTorch implementation of the paper: Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting.

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Official PyTorch implementation of the paper: Improving Graph Neural Net

Giorgos Bouritsas 58 Dec 31, 2022
A series of Python scripts to access measurements from Fluke 28X meters. Fluke IR Remote Interface required.

Fluke289_data_access A series of Python scripts to access measurements from Fluke 28X meters. Fluke IR Remote Interface required. Created from informa

3 Dec 08, 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da

Sanyam Kapoor 18 Nov 17, 2022
A public available dataset for road boundary detection in aerial images

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images

Zhenhua Xu 79 Jan 04, 2023
A curated list of the top 10 computer vision papers in 2021 with video demos, articles, code and paper reference.

The Top 10 Computer Vision Papers of 2021 The top 10 computer vision papers in 2021 with video demos, articles, code, and paper reference. While the w

Louis-François Bouchard 118 Dec 21, 2022
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide

Multimedia Computing Group, Nanjing University 44 Dec 14, 2022