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CONTRIBUTING.md

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# Contributing to Reference Implementation Catalog
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# Contributing to Implementation Catalog
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Thank you for your interest in contributing to the Vector Institute Reference Implementation Catalog!
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Thank you for your interest in contributing to the Vector Institute Implementation Catalog!
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## Adding a New Implementation
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## Additional Information
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For questions about contributing to the Reference Implementation Catalog, please reach out to the AI Engineering team at Vector Institute.
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For questions about contributing to the Implementation Catalog, please reach out to the AI Engineering team at Vector Institute.

README.md

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docs/implementation_details.md

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- **Description**: Brief introduction to the repository's purpose and links to relevant research papers
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- **Algorithms**: List of ML algorithms demonstrated in the repository
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- **Datasets**: Information on datasets used, with links to publicly available data
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- **Type**: The category of implementation:
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* **bootcamp**: Educational implementations developed for workshops and learning purposes
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* **tool**: Utility libraries and tools for broader use
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* **applied-research**: Research code tied to specific projects or papers
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- **Year**: The year the implementation was published
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## Usage Notes
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!!! note
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- Many repositories contain code for reference purposes only. To run them, updates may be required to the code and environment files.
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- Links for only publicly available datasets are provided. Many datasets used in the repositories are only available on the Vector cluster.
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## Repository Organization
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The catalog is organized by implementation type to help you quickly find the resources you need:
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* **Bootcamp implementations**: Educational resources designed for workshops and learning purposes
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* **Tool implementations**: Utility libraries and general-purpose tools
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* **Applied Research implementations**: Code tied to specific research projects or papers
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Each implementation includes algorithm tags, dataset information, and other metadata to aid in discovery.
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## Contributing
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If you are a Vector researcher or engineer and would like to add your implementation to this catalog, you can contribute by following our [contribution guidelines](https://github.com/VectorInstitute/reference-implementation-catalog/blob/main/CONTRIBUTING.md).

docs/index.md

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<div class="hero-content">
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<h1></h1>
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<p>A curated collection of high-quality AI implementations developed by researchers and engineers at the Vector Institute</p>
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<a href="#browse-implementations-by-year" class="md-button md-button--primary">Browse Implementations</a>
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<a href="#browse-implementations-by-type" class="md-button md-button--primary">Browse Implementations</a>
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<a href="https://github.com/VectorInstitute/reference-implementation-catalog" class="md-button md-button--primary">View on GitHub</a>
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</div>
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</div>
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}
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.type-tag {
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color: white;
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padding: 0.1rem 0.4rem;
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border-radius: 0.8rem;
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<div class="catalog-stats">
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<div class="stat-number">100+</div>
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<div class="stat-label">Reference Implementations</div>
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<div class="stat-label">Implementations</div>
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</div>
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<div class="stat-number">7</div>
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<div class="stat-label">Years of Research</div>
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## Browse Implementations by Year
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## Browse Implementations by Type
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=== "2024"
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=== "bootcamp"
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<div class="header">
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<h3><a href="https://github.com/VectorInstitute/rag" title="Go to Repository">RAG</a></h3>
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<h3><a href="https://github.com/VectorInstitute/rag" title="Go to Repository">rag-bootcamp</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">bootcamp</span>
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</div>
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<p>This repository contains demos for various Retrieval Augmented Generation techniques using different libraries.</p>
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</div>
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<h3><a href="https://github.com/VectorInstitute/fa" title="Go to Repository">Finetuning and Alignment</a></h3>
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<h3><a href="https://github.com/VectorInstitute/fa" title="Go to Repository">finetuning-and-alignment</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains demos for finetuning techniques for LLMs focussed on reducing computational cost.</p>
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<h3><a href="https://github.com/VectorInstitute/pe-lab" title="Go to Repository">Prompt Engineering Laboratory</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains demos for various Prompt Engineering techniques, along with examples for Bias quantification, text classification.</p>
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<h3><a href="https://github.com/VectorInstitute/bmu" title="Go to Repository">bias-mitigation-unlearning</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains code for the paper [Can Machine Unlearning Reduce Social Bias in Language Models?][bmu-paper] which was published at EMNLP'24 in the Industry track. <br>Authors are Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak.</p>
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<h3><a href="https://github.com/VectorInstitute/cyclops" title="Go to Repository">cyclops-workshop</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains demos for using [CyclOps] package for clinical ML evaluation and monitoring.</p>
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<h3><a href="https://github.com/VectorInstitute/odyssey" title="Go to Repository">odyssey</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This is a library created with research done for the paper [EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records][odyssey-paper] published at ArXiv'24. <br>Authors are Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye, Xu Cao, Arash Afkanpour, Amrit Krishnan.</p>
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<h3><a href="https://github.com/VectorInstitute/diffusion" title="Go to Repository">Diffusion model bootcamp</a></h3>
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<h3><a href="https://github.com/VectorInstitute/diffusion" title="Go to Repository">diffusion-model-bootcamp</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains demos for various diffusion models for tabular and time series data.</p>
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<h3><a href="https://github.com/VectorInstitute/nmb" title="Go to Repository">News Media Bias</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains code for libraries and experiments to recognise and evaluate bias and fakeness within news media articles via LLMs.</p>
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<h3><a href="https://github.com/VectorInstitute/nmb-plus" title="Go to Repository">News Media Bias Plus</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>Continuation of News Media Bias project, this repository contains code for libraries and experiments to collect and annotate data, recognise and evaluate bias and fakeness within news media articles via LLMs and LVMs.</p>
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<strong>Datasets:</strong> <span class="dataset-tag">News Media Bias Plus Full Data</span> <span class="dataset-tag">nmb-plus-full-data</span> <span class="dataset-tag">NMB Plus Named Entities</span> <span class="dataset-tag">nmb-plus-entities</span>
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<h3><a href="https://github.com/VectorInstitute/anomaly" title="Go to Repository">Anomaly Detection Project</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains demos for various supervised and unsupervised anomaly detection techniques in domains such as Fraud Detection, Network Intrusion Detection, System Monitoring and image, Video Analysis.</p>
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<h3><a href="https://github.com/VectorInstitute/ssl" title="Go to Repository">SSL Bootcamp</a></h3>
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<p>This repository contains demos for self-supervised techniques such as contrastive learning, masked modeling and self distillation.</p>
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<h3><a href="https://github.com/VectorInstitute/ci-lab" title="Go to Repository">Causal Inference Lab</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains code to estimate the causal effects of an intervention on some measurable outcome primarily in the health domain.</p>
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<h3><a href="https://github.com/VectorInstitute/hvaic" title="Go to Repository">HV-Ai-C</a></h3>
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<p>This repository implements a Reinforcement Learning agent to optimize energy consumption within Data Centers.</p>
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<p>This repository contains code for the paper [FlexModel: A Framework for Interpretability of Distributed Large Language Models][flex-model-paper]. <br> Authors are Matthew Choi, Muhammad Adil Asif, John Willes, David Emerson.</p>
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<h3><a href="https://github.com/VectorInstitute/vbll" title="Go to Repository">VBLL</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains code for the paper [Variational Bayesian Last Layers][vbll-paper]. <br> Authors are James Harrison, John Willes, Jasper Snoek.</p>
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<h3><a href="https://github.com/VectorInstitute/recsys" title="Go to Repository">Recommendation Systems</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains demos for various RecSys techniques such as Collaborative Filtering, Knowledge Graph, RL based, Sequence Aware, Session based etc.</p>
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<h3><a href="https://github.com/VectorInstitute/forecasting-dl" title="Go to Repository">Forecasting with Deep Learning</a></h3>
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<span class="tag type-tag">bootcamp</span>
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<p>This repository contains demos for a variety of forecasting techniques for Univariate and Multivariate time series, spatiotemporal forecasting etc.</p>
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<p>This repository contains code for the paper [Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support][naa-paper] published at EMNLP'22 in the industry track. <br> Authors are Stephen Obadinma, Faiza Khan Khattak, Shirley Wang, Tania Sidhorn, Elaine Lau, Sean Robertson, Jingcheng Niu, Winnie Au, Alif Munim, Karthik Raja Kalaiselvi Bhaskar.</p>
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<h3><a href="https://github.com/VectorInstitute/pet" title="Go to Repository">Privacy Enhancing Technologies</a></h3>
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<p>This repository contains demos for Privacy, Homomorphic Encryption, Horizontal and Vertical Federated Learning, MIA, and PATE.</p>
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<h3><a href="https://github.com/VectorInstitute/ssgvap" title="Go to Repository">SSGVQAP</a></h3>
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<p>This repository contains code for the paper [A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature][ssgvap-paper] which was accepted in ACL'20. <br> Authors are Rohan Bhambhoria, Luna Feng, Dawn Sepehr, John Chen, Conner Cowling, Sedef Kocak, Elham Dolatabadi.</p>
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<p>This repository replicates the experiments described on pages 16 and 17 of the [2022 Edition of Canada's Food Price Report][fpf-paper].</p>
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<p>This repository tackles different problems such as defect detection, footprint extraction, road obstacle detection, traffic incident detection, and segmentation of medical procedures.</p>
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