🤖 MiniHack is a sandbox framework for easily designing rich and diverse environments for Reinforcement Learning

Github: https://github.com/facebookresearch/minihack

Facebook AI: https://ai.facebook.com/blog/minihack-a-new-sandbox-for-open-ended-reinforcement-learning/

Paper: https://arxiv.org/abs/2109.13202

Documentation: https://minihack.readthedocs.io/

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TSM: Temporal Shift Module for Efficient Video Understanding

Github: https://github.com/princeton-nlp/made

Website: https://hanlab.mit.edu/projects/tsm/

Paper: https://arxiv.org/abs/1811.08383

Dataset: https://paperswithcode.com/dataset/squad

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🚀 Fast bottom-up method that jointly detects over 100 keypoints on humans or objects

Github: https://github.com/duncanzauss/keypoint_communities

Paper: https://arxiv.org/abs/2110.00988v1

Dataset: https://paperswithcode.com/dataset/coco

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🔍 FLAN: More generalizable Language Models with Instruction Fine-Tuning

Google research: https://ai.googleblog.com/2021/10/introducing-flan-more-generalizable.html

Github: https://github.com/google-research/FLAN

Paper: https://arxiv.org/abs/2109.01652


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🎯 Darts: User-Friendly Modern Machine Learning for Time Series

Github: https://github.com/unit8co/darts

Paper: https://arxiv.org/abs/2110.03224v1

Examples: https://unit8co.github.io/darts/examples.html

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➡️ Pytorch Transfer Learning Library

Github: https://github.com/thuml/Transfer-Learning-Library

Paper: https://arxiv.org/abs/2110.02578v1

Dataset: https://paperswithcode.com/dataset/cityscapes

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🧷 SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.

Github: https://github.com/slundberg/shap

Paper: https://arxiv.org/abs/2110.03309v1

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🌲 Learning High-Speed Flight in the Wild

Github: https://github.com/uzh-rpg/agile_autonomy

Project: http://rpg.ifi.uzh.ch/AgileAutonomy.html

Paper: http://rpg.ifi.uzh.ch/docs/Loquercio21_Science.pdf

Dataset: https://zenodo.org/record/5517791#.YV2zkGNfhhE

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LightSeq: A High Performance Library for Sequence Processing and Generation

Github: https://github.com/bytedance/lightseq

Paper: https://arxiv.org/abs/2110.05722v1

A Guide of LightSeq Training: https://github.com/bytedance/lightseq/blob/master/docs/guide.md

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📊ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Github: https://github.com/ifzhang/ByteTrack

Paper: https://arxiv.org/abs/2110.06864

Dataset: https://paperswithcode.com/dataset/motchallenge

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🍾 Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)

Github: https://github.com/jbeomlee93/rib

Paper: http://arxiv.org/abs/2110.06530

Dataset: https://paperswithcode.com/dataset/coco

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📖 Bayesian Optimization Book 2021

Book: https://bayesoptbook.com

Github: https://github.com/bayesoptbook/bayesoptbook.github.io

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ESPnet2-TTS: Extending the Edge of TTS Research

Github: https://github.com/espnet/espnet

Docs: https://espnet.github.io/espnet/

Paper: https://arxiv.org/abs/2110.07840v1

Dataset: https://paperswithcode.com/dataset/vctk

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🧵 abess: Fast Best-Subset Selection in Python and R

Github: https://github.com/abess-team/abess

Docs: https://abess.readthedocs.io/en/latest/Tutorial/index.html

Paper: https://arxiv.org/abs/2110.09697v1


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📄 HRFormer: High-Resolution Transformer for Dense Prediction, NeurIPS 2021

Github: https://github.com/HRNet/HRFormer

Paper: https://arxiv.org/abs/2110.09408v1

Dataset: https://paperswithcode.com/dataset/coco

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⚙️SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning

Github: https://github.com/FederatedAI/FATE

Paper: https://arxiv.org/abs/2110.10927v1

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🅱️ Bi-directional Image and Text Generation

Github: https://github.com/researchmm/generate-it

Paper: https://arxiv.org/abs/2110.09753v1

Dataset: https://paperswithcode.com/dataset/coco

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📌 PyTorch: Transfer Learning and Image Classification

https://www.pyimagesearch.com/2021/10/11/pytorch-transfer-learning-and-image-classification/

Dataset: https://www.tensorflow.org/datasets/catalog/tf_flowers

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🚀 NAS-FCOS: Fast Neural Architecture Search for Object Detection

Github: https://github.com/Lausannen/NAS-FCOS

Paper: https://arxiv.org/abs/2110.12423v1


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🌲 Revisiting randomized choices in isolation forests

Github: https://github.com/david-cortes/isotree

Paper: https://arxiv.org/abs/2110.13402v1

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😃 GoEmotions: A Dataset for Fine-Grained Emotion Classification

Github: https://github.com/google-research/google-research/tree/master/goemotions

Google AI: https://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html

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