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11 | 11 | | [SSGVQAP][ssgvap-repo] | 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 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] | |
12 | 12 | | [NeuralKernelBandits][nkb-repo] | This repository contains code for the paper [An Empirical Study of Neural Kernel Bandits][nkb-paper] 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], [Statlog] <br> [Adult][adult-nkb], [US Census 1990] <br> [Covertype] | |
13 | 13 | | [foodprice-forecasting][fpf-repo] | This repository replicates the experiments described on pages 16 and 17 of the [2022 Edition of Canada's Food Price Report][fpf-paper]. | 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] | |
| 14 | +| [Recommendation Systems][recsys-repo] | This repository contains demos for various RecSys techniques such as Collaborative Filtering, Knowledge Graph, RL based, Sequence Aware, Session based etc. | SVD++, NeuMF, Plot based, Two tower, SVD, KG based, SlateQ, BST, Simple Association Rules, first-order Markov Chains, Sequential Rules, RNN, Neural Attentive Session, BERT4rec, A2SVDModel, SLi-Rec | 7 | [Amazon-recsys] ,[careervillage], <br> [movielens-recsys], [tmdb], [LastFM] <br> [yoochoose] | |
| 15 | +| [Forecasting with Deep Learning][forecasting-dl-repo] | This repository contains demos for a variety of forecasting techniques for Univariate and Multivariate time series, spatiotemporal forecasting etc. | Exponential Smoothing, Persistence Forecasting, Mean Window Forecast, Prophet, Neuralphophet, NBeats, DeepAR, Autoformer, DLinear, NHITS | 11 | [Canadian Weather Station Data], [BoC Exchange rate], [Electricity Consumption], [Road Traffic Occupancy], [Influenza-Like Illness Patient Ratios], [Walmart M5 Retail Product Sales], [WeatherBench], [Grocery Store Sales], [Economic Data with Food CPI] | |
| 16 | +| [Prompt Engineering][pe-repo] | This repository contains demos for a variety of Prompt Engineering techniques such as fairness measurement via sentiment analysis, finetuning, prompt tuning, prompt ensembling etc. | Bias Quantification & Probing, Stereotypical Bias Analysis, Binary sentiment analysis task, Finetuning using HF Library, Gradient-Search for Instruction Prefix, GRIPS for Instruction Prefix, LLM Summarization, LLM Classification: AG News task, LLM BoolQ (Boolean question answering), LLM Basic Translation (French -> English), LLM Aspect-Based Sentiment Analysis, prompt-tuning, Activation Computation, LLM Classifier Training, Voting and Averaging Ensembles | 10 | [Crow-pairs], [sst5], [cnn_dailymail], [ag_news], [Tweet-data], [Other] | |
| 17 | +| [ABSA][absa-repo] | This repository contains code for the paper [Open Aspect Target Sentiment Classification with Natural Language Prompts][absa-paper]. <br> Authors are Ronald Seoh, Ian Birle, Mrinal Tak, Haw-Shiuan Chang, Brian Pinette, Alfred Hough. | Zero-shot inference for sentiment using PLM and openprompt, Few-shot inference for sentiment using PLM, Zero-shot ATSC with Prompts using BERT and OPT, Zero-shot inference of aspect term and generate sentiment polarity using NLTK pipeline | 4 | [Link][Link-absa] | |
| 18 | +| [NAA][naa-repo] | 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] 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. | Context Retreival using SBERT bi-encoder, Context Retreival using SBERT cross-encoder, Intent identification using BERT, Few Shot Multi-Class Text Classification with BERT, Multi-Class Text Classification with BERT, Reponse generation via GPT2. | 5 | [ELI5], [MSMARCO] | |
14 | 19 | -------- |
15 | 20 |
|
16 | 21 | [//]: # (Reference links for Github repositories) |
|
21 | 26 | [ssgvap-repo]: https://github.com/VectorInstitute/SSGVQAP |
22 | 27 | [nkb-repo]: https://github.com/VectorInstitute/NeuralKernelBandits |
23 | 28 | [fpf-repo]: https://github.com/VectorInstitute/foodprice-forecasting |
| 29 | +[recsys-repo]: https://github.com/VectorInstitute/recommender_systems_project |
| 30 | +[forecasting-dl-repo]: https://github.com/VectorInstitute/forecasting-with-dl |
| 31 | +[pe-repo]: https://github.com/VectorInstitute/PromptEngineering |
| 32 | +[fastgan-repo]: https://github.com/VectorInstitute/FastGAN-pytorch |
| 33 | +[absa-repo]: https://github.com/VectorInstitute/ABSA |
| 34 | +[naa-repo]: https://github.com/VectorInstitute/NAA |
24 | 35 |
|
25 | 36 | [//]: # (Reference links for Research papers) |
26 | 37 | [laftr-paper]: https://arxiv.org/abs/1802.06309 |
27 | 38 | [god-paper]: http://proceedings.mlr.press/v119/sastry20a.html |
28 | 39 | [ssgvap-paper]: https://aclanthology.org/2020.sdp-1.4/ |
29 | 40 | [nkb-paper]: https://arxiv.org/abs/2111.03543 |
30 | 41 | [fpf-paper]: https://www.dal.ca/sites/agri-food/research/canada-s-food-price-report-2022.html |
| 42 | +[absa-paper]: https://aclanthology.org/2021.emnlp-main.509/ |
31 | 43 |
|
32 | 44 | [//]: # (Reference links for datasets) |
33 | 45 | [CIFAR10]: https://pytorch.org/vision/main/generated/torchvision.datasets.CIFAR10.html#torchvision.datasets.CIFAR10 |
|
56 | 68 | [Adult-nkb]: https://archive.ics.uci.edu/dataset/2/adult |
57 | 69 | [US Census 1990]: https://archive.ics.uci.edu/dataset/116/us+census+data+1990 |
58 | 70 | [Covertype]: https://archive.ics.uci.edu/dataset/31/covertype |
59 | | -[FRED Economic Data]: https://fred.stlouisfed.org/ |
| 71 | +[FRED Economic Data]: https://fred.stlouisfed.org/ |
| 72 | +[Amazon-recsys]: https://drive.google.com/drive/folders/1w9ofYRBZN5XIb8M-UzbU3Wp4H1ZBYOFi?usp=drive_link |
| 73 | +[careervillage]: https://drive.google.com/drive/folders/1rNeBtNYM7Z0oHVho75PDEP3VZIXAoxx9?usp=drive_link |
| 74 | +[movielens-recsys]: https://drive.google.com/drive/folders/112OtYq83WZgVqV43pGhKZjVTzlUhKM-b?usp=drive_link |
| 75 | +[tmdb]: https://drive.google.com/drive/folders/1CU863OynVNnNTTduKxExubCyJLCZPF0R?usp=drive_link |
| 76 | +[LastFM]: https://drive.google.com/drive/folders/1Jftz1_olxblJVZe6ZDMdrclAW_YnCOci?usp=drive_link |
| 77 | +[yoochoose]: https://drive.google.com/drive/folders/1XNyPH8i-pxnNbJKjZZRCL1oz-HPZscLC?usp=drive_link |
| 78 | +[Canadian Weather Station Data]: https://drive.google.com/drive/folders/1YeOoJNf7VCy7r3sFhdTrl7WdevcUIZNW |
| 79 | +[BoC Exchange rate]: https://drive.google.com/drive/folders/1Z9pnC0kPN-c_eAHSsPyWPYRnnGR3sEuf |
| 80 | +[Electricity Consumption]: https://drive.google.com/drive/folders/1YIl6RHAQ5muZEjFjXLj7Zt4vOwKUu2Qe |
| 81 | +[Road Traffic Occupancy]: https://drive.google.com/drive/folders/1YDM-mMGuhlE_pTlwb5qoPcOQfspJ4m4W |
| 82 | +[Influenza-Like Illness Patient Ratios]: https://drive.google.com/drive/folders/1YFoC3fWY-22S11MtfKHnl_R8OminZ2eo |
| 83 | +[Walmart M5 Retail Product Sales]: https://drive.google.com/drive/folders/1bc488T1GsJ3xg2nQmuFTut7uF1SFDSp2 |
| 84 | +[WeatherBench]:https://drive.google.com/drive/folders/1YD-Hadx_T4JZcjmvFYDp4Pb71852CIVT |
| 85 | +[Grocery Store Sales]: https://drive.google.com/drive/folders/1as_cJgJbzw1OlnWyF8Y3xj7ZEjRh_kD6 |
| 86 | +[Economic Data with Food CPI]: https://drive.google.com/drive/folders/1cNyHR5DpUQ5RORgDS8pB8iswWo-iBLFI |
| 87 | +[Crow-pairs]: https://github.com/VectorInstitute/PromptEngineering/blob/main/src/reference_implementations/fairness_measurement/crow_s_pairs/resources/crows_pairs_anonymized.csv |
| 88 | +[sst5]: http://github.com/VectorInstitute/PromptEngineering/blob/main/src/reference_implementations/fairness_measurement/czarnowska_analysis/resources/processed_sst5.tsv |
| 89 | +[cnn_dailymail]: https://huggingface.co/datasets/ccdv/cnn_dailymail |
| 90 | +[ag_news]: https://huggingface.co/datasets/fancyzhx/ag_news |
| 91 | +[Tweet-data]: https://github.com/VectorInstitute/PromptEngineering/tree/main/resources/datasets |
| 92 | +[Other]: https://github.com/VectorInstitute/PromptEngineering/tree/main/src/reference_implementations/prompting_vector_llms/llm_prompting_examples/resources |
| 93 | + |
| 94 | + |
| 95 | +[Few shot images dataset]: https://drive.google.com/file/d/1aAJCZbXNHyraJ6Mi13dSbe7pTyfPXha0/view |
| 96 | +[Link-absa]: https://github.com/VectorInstitute/ABSA/tree/main/atsc_paper/atsc_prompts_modified/dataset_files |
| 97 | +[ELI5]: https://drive.google.com/drive/folders/1PDBiij-6JSxOtplOSc0hPTk9zL9n3qR6 |
| 98 | +[MSMARCO]: https://drive.google.com/drive/folders/1LO3OtuDC_FSFktTgb2NfjPY2cse7WcTY |
| 99 | + |
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