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‎README.md‎

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| Repository | Description | Algorithms | Number of<br>datasets | Datasets |
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| :--------- | :---------- | :---------: | :--------------------:| :-------: |
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| [laftr](https://github.com/VectorInstitute/gram-ood-detection) | This repository contains code for the paper [Learning Adversarially Fair and Transferable Representations](https://arxiv.org/abs/1802.06309), which was accepted at PMLR'18. <br> Authors are David Madras, Elliot Creager, Toniann Pitassi and Richard Zemel. | laftr | 1 | tabular |
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| [gram-ood-detection](https://github.com/VectorInstitute/gram-ood-detection) | This repository contains code for the paper [Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices](http://proceedings.mlr.press/v119/sastry20a.html) which was accepted at ICML'20. <br> Authors are Chandramouli Shama Sastry, Sageev Oore. | OOD detection using Gram matrices | 7 | images |
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| [Computer_Vision_Project](https://github.com/VectorInstitute/Computer_Vision_Project) | This repository tackles different problems such as defect detection, footprint extraction, road obstacle detection, traffic incident detection, and segmentation of medical procedures. | Semantic segmentation using Unet, Unet++, FCN, DeepLabv3; Anomaly segmentation | 11 | images, videos |
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| [Privacy Enhancing Technologies](https://github.com/VectorInstitute/Computer_Vision_Project) | This repository contains demos for Privacy, Homomorphic Encryption, Horizontal and Vertical Federated Learning, MIA, and PATE | Vanilla SGD, DP SGD, DP Logistic Regression, Homomorphic Encryption for MLP, Horizontal FL, Horizontal FL on MLP, Membership Inference Attacks (MIA) using DP, MIA using SAM, PATE, Vertical FL. | 9 | tabular, images |
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| [SSGVQAP](https://github.com/VectorInstitute/SSGVQAP) | This repository contains code for the paper [A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature](https://aclanthology.org/2020.sdp-1.4/) which was accepted ibn ACL'20. Authors are Rohan Bhambhoria, Luna Feng, Dawn Sepehr, John Chen, Conner Cowling, Sedef Kocak, Elham Dolatabadi. | An Active Learning Strategy for Data Selection, AL-Uncertainty, AL-Clustering | 1 | tabular |
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| [NeuralKernelBandits](https://github.com/VectorInstitute/NeuralKernelBandits) | This repository contains code for the paper [An Empirical Study of Neural Kernel Bandits](https://arxiv.org/abs/2111.03543) which was accepted in Neurips'21. Authors are Lisicki, Michal, Arash Afkanpour, and Graham W. Taylor. | Neural tangent kernel, Conjugate kernel, NNGP, Deep ensembles, Randomized Priors, NTKGP, Upper Confidence Bounds (UCB), Thompson Sampling (TS) | 7 | tabular |
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| [foodprice-forecasting](https://github.com/VectorInstitute/foodprice-forecasting) | This repository replicates the experiments described on pages 16 and 17 of the [2022 Edition of Canada's Food Price Report](https://www.dal.ca/sites/agri-food/research/canada-s-food-price-report-2022.html). | Time series forecasting using Prophet, Time series forecasting using Neural prophet, Interpretable time series forecasting using N-BEATS, Ensemble of the above methods. | 3 | tabular |
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| [laftr](https://github.com/VectorInstitute/laftr) | This repository contains code for the paper [Learning Adversarially Fair and Transferable Representations](https://arxiv.org/abs/1802.06309), which was accepted at PMLR'18. <br> Authors are David Madras, Elliot Creager, Toniann Pitassi and Richard Zemel. | laftr | 1 | [Adult](https://github.com/VectorInstitute/laftr/tree/master/data/adult) |
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| [gram-ood-detection](https://github.com/VectorInstitute/gram-ood-detection) | This repository contains code for the paper [Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices](http://proceedings.mlr.press/v119/sastry20a.html) which was accepted at ICML'20. <br> Authors are Chandramouli Shama Sastry, Sageev Oore. | OOD detection using Gram matrices | 7 | [CIFAR10](https://pytorch.org/vision/main/generated/torchvision.datasets.CIFAR10.html#torchvision.datasets.CIFAR10) [CIFAR100](https://pytorch.org/vision/main/generated/torchvision.datasets.CIFAR100.html#torchvision.datasets.CIFAR100) [SVHN](https://pytorch.org/vision/main/generated/torchvision.datasets.SVHN.html#torchvision.datasets.SVHN) |
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| [Computer_Vision_Project](https://github.com/VectorInstitute/Computer_Vision_Project) | This repository tackles different problems such as defect detection, footprint extraction, road obstacle detection, traffic incident detection, and segmentation of medical procedures. | Semantic segmentation using Unet, Unet++, FCN, DeepLabv3; Anomaly segmentation | 11 | [SpaceNet Building Detection V2](https://spacenet.ai/spacenet-buildings-dataset-v2/) [MVTEC](https://www.mvtec.com/company/research/datasets/mvtec-ad) [ICDAR2015](https://drive.google.com/drive/folders/12eg7u7oBkZ6-ov3ITiED4nLlQzP4KoTd) [PASCAL_VOC](https://drive.google.com/drive/folders/12eg7u7oBkZ6-ov3ITiED4nLlQzP4KoTd) [DOTA](https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly) [AVA](https://github.com/cvdfoundation/ava-dataset) [UCF101-24](https://drive.google.com/file/d/1o2l6nYhd-0DDXGP-IPReBP4y1ffVmGSE/view?usp=sharing) [J-HMDB-21](http://jhmdb.is.tue.mpg.de/challenge/JHMDB/datasets)|
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| [Privacy Enhancing Technologies](https://github.com/VectorInstitute/PETs-Bootcamp) | This repository contains demos for Privacy, Homomorphic Encryption, Horizontal and Vertical Federated Learning, MIA, and PATE | Vanilla SGD, DP SGD, DP Logistic Regression, Homomorphic Encryption for MLP, Horizontal FL, Horizontal FL on MLP, Membership Inference Attacks (MIA) using DP, MIA using SAM, PATE, Vertical FL. | 9 | [Heart Disease](https://www.kaggle.com/datasets/ronitf/heart-disease-uci) [Credit Card Fraud](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) [Breaset Cancer Data](https://github.com/VectorInstitute/PETs-Bootcamp/blob/main/HE_TenSEAL/breast_cancer_data.csv) [TCGA](https://vectorinstituteai-my.sharepoint.com/personal/sedef_kocak_vectorinstituteai_onmicrosoft_com/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fsedef%5Fkocak%5Fvectorinstituteai%5Fonmicrosoft%5Fcom%2FDocuments%2FPETS%5FProject%5FParticipants%2FExample%20Datasets%2FKidney%20Histopathology&ga=1) [CIFAR10](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/cifar10) [Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk/overview) [Yelp](https://business.yelp.com/data/resources/open-dataset/) [Airbnb](https://insideairbnb.com/get-the-data/) |
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| [SSGVQAP](https://github.com/VectorInstitute/SSGVQAP) | This repository contains code for the paper [A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature](https://aclanthology.org/2020.sdp-1.4/) which was accepted ibn ACL'20. Authors are Rohan Bhambhoria, Luna Feng, Dawn Sepehr, John Chen, Conner Cowling, Sedef Kocak, Elham Dolatabadi. | An Active Learning Strategy for Data Selection, AL-Uncertainty, AL-Clustering | 1 | [CORD-19](https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge) |
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| [NeuralKernelBandits](https://github.com/VectorInstitute/NeuralKernelBandits) | This repository contains code for the paper [An Empirical Study of Neural Kernel Bandits](https://arxiv.org/abs/2111.03543) which was accepted in Neurips'21. Authors are Lisicki, Michal, Arash Afkanpour, and Graham W. Taylor. | Neural tangent kernel, Conjugate kernel, NNGP, Deep ensembles, Randomized Priors, NTKGP, Upper Confidence Bounds (UCB), Thompson Sampling (TS) | 7 | [Mushroom](https://archive.ics.uci.edu/dataset/73/mushroom) [Statlog](https://github.com/VectorInstitute/NeuralKernelBandits/tree/main/contextual_bandits/datasets) [Adult](https://archive.ics.uci.edu/dataset/2/adult) [US Census 1990](https://archive.ics.uci.edu/dataset/116/us+census+data+1990) [Covertype](https://archive.ics.uci.edu/dataset/31/covertype) |
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| [foodprice-forecasting](https://github.com/VectorInstitute/foodprice-forecasting) | This repository replicates the experiments described on pages 16 and 17 of the [2022 Edition of Canada's Food Price Report](https://www.dal.ca/sites/agri-food/research/canada-s-food-price-report-2022.html). | Time series forecasting using Prophet, Time series forecasting using Neural prophet, Interpretable time series forecasting using N-BEATS, Ensemble of the above methods. | 3 | [FRED Economic Data](https://fred.stlouisfed.org/) |
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