📥 Don’t be Contradicted with Anything!CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Github: https://github.com/yizhen20133868/ci-tod
Paper: https://arxiv.org/abs/2109.11292v1
Dataset: https://paperswithcode.com/dataset/kvret-1
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Github: https://github.com/yizhen20133868/ci-tod
Paper: https://arxiv.org/abs/2109.11292v1
Dataset: https://paperswithcode.com/dataset/kvret-1
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Unseen Object Amodal Instance Segmentation (UOAIS)
Github: https://github.com/gist-ailab/uoais
Paper: https://arxiv.org/abs/2109.11103
Dataset: https://paperswithcode.com/dataset/ocid
Project: https://sites.google.com/view/uoais
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Github: https://github.com/gist-ailab/uoais
Paper: https://arxiv.org/abs/2109.11103
Dataset: https://paperswithcode.com/dataset/ocid
Project: https://sites.google.com/view/uoais
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👍1
AlphaRotate: A Rotation Detection Benchmark using TensorFlow
Github: https://github.com/yangxue0827/RotationDetection
Paper: https://arxiv.org/abs/2109.11906v1
Dataset: https://paperswithcode.com/dataset/dota
Documentation: https://rotationdetection.readthedocs.io/en/latest/
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Github: https://github.com/yangxue0827/RotationDetection
Paper: https://arxiv.org/abs/2109.11906v1
Dataset: https://paperswithcode.com/dataset/dota
Documentation: https://rotationdetection.readthedocs.io/en/latest/
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🧍♂ PASS: Pictures without humAns for Self-Supervised Pretraining
PASS is a large-scale image dataset that does not include any humans, human parts, or other personally identifiable information.
Github: https://github.com/yukimasano/PASS
Paper: https://arxiv.org/abs/2109.13228v1
Dataset: https://paperswithcode.com/dataset/pass
Documentation: https://www.robots.ox.ac.uk/~vgg/research/pass/
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PASS is a large-scale image dataset that does not include any humans, human parts, or other personally identifiable information.
Github: https://github.com/yukimasano/PASS
Paper: https://arxiv.org/abs/2109.13228v1
Dataset: https://paperswithcode.com/dataset/pass
Documentation: https://www.robots.ox.ac.uk/~vgg/research/pass/
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🧠 С момента выкладки библиотеки CatBoost в опенсорс прошло 100 лет! (Это если считать в двоичной системе счисления). Но главная новость в другом: библиотека обновилась до версии 1.0.0 и достигла состояния «production ready».
Более подробно обо всём этом читайте на Хабре: https://habr.com/ru/company/yandex/blog/580950/
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Более подробно обо всём этом читайте на Хабре: https://habr.com/ru/company/yandex/blog/580950/
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⛈ Nowcasting the Next Hour of Rain
Github: https://github.com/deepmind/deepmind-research/tree/master/nowcasting
Paper: https://www.nature.com/articles/s41586-021-03854-z
Deepmind blog: https://deepmind.com/blog/article/nowcasting
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Github: https://github.com/deepmind/deepmind-research/tree/master/nowcasting
Paper: https://www.nature.com/articles/s41586-021-03854-z
Deepmind blog: https://deepmind.com/blog/article/nowcasting
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🤖 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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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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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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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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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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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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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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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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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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👍1
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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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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👍1
📊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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Github: https://github.com/ifzhang/ByteTrack
Paper: https://arxiv.org/abs/2110.06864
Dataset: https://paperswithcode.com/dataset/motchallenge
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👍1
🍾 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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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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Book: https://bayesoptbook.com
Github: https://github.com/bayesoptbook/bayesoptbook.github.io
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❤1
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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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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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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Github: https://github.com/HRNet/HRFormer
Paper: https://arxiv.org/abs/2110.09408v1
Dataset: https://paperswithcode.com/dataset/coco
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