🌠 NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques.
Github: https://github.com/netket/netket
Paper: https://arxiv.org/pdf/2112.10526v1.pdf
Homepage: https://www.netket.org
Documentation: https://www.netket.org/documentation
Tutorials: https://www.netket.org/tutorials
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NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques.
Github: https://github.com/netket/netket
Paper: https://arxiv.org/pdf/2112.10526v1.pdf
Homepage: https://www.netket.org
Documentation: https://www.netket.org/documentation
Tutorials: https://www.netket.org/tutorials
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📐 RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality (PyTorch)
Github: https://github.com/DingXiaoH/RepMLP
Pre-trained model: https://drive.google.com/drive/folders/1eDFunxOQ67MvBBmJ4Bw01TFh2YVNRrg2?usp=sharing
Paper: https://arxiv.org/abs/2112.11081v1
Task: https://paperswithcode.com/task/semantic-segmentation
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Github: https://github.com/DingXiaoH/RepMLP
Pre-trained model: https://drive.google.com/drive/folders/1eDFunxOQ67MvBBmJ4Bw01TFh2YVNRrg2?usp=sharing
Paper: https://arxiv.org/abs/2112.11081v1
Task: https://paperswithcode.com/task/semantic-segmentation
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📑 GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Github: https://github.com/openai/glide-text2im
Notebooks: https://github.com/openai/glide-text2im/blob/main/notebooks
Paper: https://arxiv.org/abs/2112.10741
Task: https://paperswithcode.com/task/image-generation
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Github: https://github.com/openai/glide-text2im
Notebooks: https://github.com/openai/glide-text2im/blob/main/notebooks
Paper: https://arxiv.org/abs/2112.10741
Task: https://paperswithcode.com/task/image-generation
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📹 MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
Github: https://github.com/mseg-dataset/mseg-api
Paper: https://arxiv.org/abs/2112.13762
Dataset: https://paperswithcode.com/dataset/sun-rgb-d
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Github: https://github.com/mseg-dataset/mseg-api
Paper: https://arxiv.org/abs/2112.13762
Dataset: https://paperswithcode.com/dataset/sun-rgb-d
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📏 AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
Github: https://github.com/leaplabthu/adafocusv2
Paper: https://arxiv.org/abs/2112.14238v1
Tasks: https://paperswithcode.com/task/video-recognition
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Github: https://github.com/leaplabthu/adafocusv2
Paper: https://arxiv.org/abs/2112.14238v1
Tasks: https://paperswithcode.com/task/video-recognition
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🔼 PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
PyramidTNT achieves better performances than the previous state-of-the-art vision transformers such as Swin Transformer
Github: https://github.com/huawei-noah/CV-backbones
Paper: https://arxiv.org/abs/2201.00978v1
GhostNet: https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch
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PyramidTNT achieves better performances than the previous state-of-the-art vision transformers such as Swin Transformer
Github: https://github.com/huawei-noah/CV-backbones
Paper: https://arxiv.org/abs/2201.00978v1
GhostNet: https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch
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🗣 AV-HuBERT (Audio-Visual Hidden Unit BERT)
AV-HuBERT is a self-supervised representation learning framework for audio-visual speech.
Github: https://github.com/facebookresearch/av_hubert
Facebook AI: https://ai.facebook.com/blog/ai-that-understands-speech-by-looking-as-well-as-hearing/
Paper: https://arxiv.org/abs/2201.02184
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AV-HuBERT is a self-supervised representation learning framework for audio-visual speech.
Github: https://github.com/facebookresearch/av_hubert
Facebook AI: https://ai.facebook.com/blog/ai-that-understands-speech-by-looking-as-well-as-hearing/
Paper: https://arxiv.org/abs/2201.02184
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🐭 pymdp: A Python library for active inference in discrete state spaces
Github: https://github.com/infer-actively/pymdp
Paper: https://arxiv.org/abs/2201.03904v1
Docs: https://pymdp-rtd.readthedocs.io/
Tasks: https://paperswithcode.com/task/bayesian-inference
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Github: https://github.com/infer-actively/pymdp
Paper: https://arxiv.org/abs/2201.03904v1
Docs: https://pymdp-rtd.readthedocs.io/
Tasks: https://paperswithcode.com/task/bayesian-inference
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✨ Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning
Github: https://github.com/sense-x/uniformer
Paper: https://arxiv.org/abs/2201.04676v1
Tasks: https://paperswithcode.com/dataset/kinetics-600
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Github: https://github.com/sense-x/uniformer
Paper: https://arxiv.org/abs/2201.04676v1
Tasks: https://paperswithcode.com/dataset/kinetics-600
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💡 Introducing StylEx: A New Approach for Visual Explanation of Classifiers
Github: https://explaining-in-style.github.io/
Code: https://github.com/google/explaining-in-style
Article: https://ai.googleblog.com/2022/01/introducing-stylex-new-approach-for.html
Video: https://explaining-in-style.github.io/#video
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Github: https://explaining-in-style.github.io/
Code: https://github.com/google/explaining-in-style
Article: https://ai.googleblog.com/2022/01/introducing-stylex-new-approach-for.html
Video: https://explaining-in-style.github.io/#video
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🔝 Omnivore: A Single Model for Many Visual Modalities
Github: https://github.com/facebookresearch/omnivore
Code: https://github.com/facebookresearch/omnivore/blob/main/inference_tutorial.ipynb
Paper: https://arxiv.org/abs/2201.08377
Dataset: https://paperswithcode.com/dataset/epic-kitchens-100
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Github: https://github.com/facebookresearch/omnivore
Code: https://github.com/facebookresearch/omnivore/blob/main/inference_tutorial.ipynb
Paper: https://arxiv.org/abs/2201.08377
Dataset: https://paperswithcode.com/dataset/epic-kitchens-100
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🔥 The first high-performance self-supervised algorithm that works for speech, vision, and text
Github: https://github.com/pytorch/fairseq/tree/main/examples/data2vec
Paper: https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language
Meta AI: https://ai.facebook.com/blog/the-first-high-performance-self-supervised-algorithm-that-works-for-speech-vision-and-text/
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Github: https://github.com/pytorch/fairseq/tree/main/examples/data2vec
Paper: https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language
Meta AI: https://ai.facebook.com/blog/the-first-high-performance-self-supervised-algorithm-that-works-for-speech-vision-and-text/
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ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer
Github: https://github.com/locuslab/convmixer
Paper: https://arxiv.org/pdf/2201.09792v1.pdf
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Github: https://github.com/locuslab/convmixer
Paper: https://arxiv.org/pdf/2201.09792v1.pdf
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⚪ Pearl: Parallel Evolutionary and Reinforcement Learning Library
Github: https://github.com/locuslab/convmixer
Paper: https://arxiv.org/pdf/2201.09568v1.pdf
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Github: https://github.com/locuslab/convmixer
Paper: https://arxiv.org/pdf/2201.09568v1.pdf
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📎 When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism
Github: https://github.com/microsoft/SPACH
Paper: https://arxiv.org/abs/2201.10801v1
Dataset: https://paperswithcode.com/dataset/coco
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Github: https://github.com/microsoft/SPACH
Paper: https://arxiv.org/abs/2201.10801v1
Dataset: https://paperswithcode.com/dataset/coco
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🔉 PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Github: https://github.com/keonlee9420/PortaSpeech
Paper: https://arxiv.org/pdf/2109.15166v4.pdf
Dataset: https://paperswithcode.com/dataset/ljspeech
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Github: https://github.com/keonlee9420/PortaSpeech
Paper: https://arxiv.org/pdf/2109.15166v4.pdf
Dataset: https://paperswithcode.com/dataset/ljspeech
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Исследователи в области компьютерных наук могут получить миллион рублей от Яндекса!
До 20 марта открыт прием заявок на научную премию имени Ильи Сегаловича. Подать заявку могут студенты, аспиранты и научные руководители, которые занимаются распознаванием и синтезом речи, информационным поиском, машинным обучением, компьютерным зрением, обработкой естественного языка и машинным переводом.
Лауреаты премии получат:
— один миллион рублей
— оплачиваемую поездку на международную конференцию по AI
— гранты на использование сервисов Яндекс.Толока и Yandex DataSphere для своих исследований
Подробности по ссылке: https://clck.ru/amsXS
До 20 марта открыт прием заявок на научную премию имени Ильи Сегаловича. Подать заявку могут студенты, аспиранты и научные руководители, которые занимаются распознаванием и синтезом речи, информационным поиском, машинным обучением, компьютерным зрением, обработкой естественного языка и машинным переводом.
Лауреаты премии получат:
— один миллион рублей
— оплачиваемую поездку на международную конференцию по AI
— гранты на использование сервисов Яндекс.Толока и Yandex DataSphere для своих исследований
Подробности по ссылке: https://clck.ru/amsXS
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📹 VRT: A Video Restoration Transformer
Github: https://github.com/jingyunliang/vrt
Paper: https://arxiv.org/abs/2201.12288
Dataset: https://paperswithcode.com/dataset/gopro
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Github: https://github.com/jingyunliang/vrt
Paper: https://arxiv.org/abs/2201.12288
Dataset: https://paperswithcode.com/dataset/gopro
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📍 Competition-Level Code Generation with AlphaCode
Github: https://github.com/deepmind/code_contests
Paper: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf
Dataset: https://paperswithcode.com/dataset/humaneval
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Github: https://github.com/deepmind/code_contests
Paper: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf
Dataset: https://paperswithcode.com/dataset/humaneval
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VOS: Learning What You Don't Know by Virtual Outlier Synthesis
Github: https://github.com/deeplearning-wisc/vos
Paper: https://arxiv.org/pdf/2202.01197v2.pdf
Dataset: https://paperswithcode.com/dataset/bdd100k
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Github: https://github.com/deeplearning-wisc/vos
Paper: https://arxiv.org/pdf/2202.01197v2.pdf
Dataset: https://paperswithcode.com/dataset/bdd100k
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