The GENRE (Generarive ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.

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

Paper: https://arxiv.org/abs/2103.12528v1

Dataset: https://github.com/facebookresearch/GENRE/blob/main/scripts/download_all_datasets.sh

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Каждый день мы загружаем и передаем все больше контента. Нагрузка на базовые станции операторов растет, но вместе с ней растет и качество связи. Как это получается? И какую роль в этом процессе играет big data?

Ответы найдете в видео «Как нейросети помогают оставаться на связи», где Тимур и Вика, датасайентисты из МегаФона, рассказывают, почему модернизация базовых станций – это задача из области data science, как работает модель, на основании рекомендаций которой принимаются решения о необходимости «прокачки» той или иной вышки, и остается ли в этом процессе место для человека.

Спойлер: место для человека остается. Например, в МегаФоне в команду по работе с большими данными ищут Team Lead, Senior data scientist, инженера SQL и других специалистов.

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Github: https://github.com/czczup/URST

Paper: https://arxiv.org/abs/2103.11784

Dataset: https://cocodataset.org/#download

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🤗 ⚡️Multilingual CLIP with Huggingface + PyTorch Lightning

https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html#Data

CLIP: Connecting Text and Images: https://openai.com/blog/clip/


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Swin Transformer - PyTorch

Code: https://github.com/berniwal/swin-transformer-pytorch

Github: https://github.com/microsoft/Swin-Transformer

Paper: https://arxiv.org/abs/2103.14030v1

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UniverseNet

UniverseNet is the state-of-the-art detector that can be trained in 24 epochs.

Github: https://github.com/shinya7y/UniverseNet

Paper: https://arxiv.org/abs/2103.14027v1

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TransT - Transformer Tracking

Github: https://github.com/chenxin-dlut/TransT

Paper: https://arxiv.org/abs/2103.15436

Model: https://drive.google.com/drive/folders/1GVQV1GoW-ttDJRRqaVAtLUtubtgLhWCE

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Hyperbolic Graph Convolutional Auto-Encoders

Github: https://github.com/junhocho/HGCAE

Paper: https://arxiv.org/abs/2103.16046

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⚡️ NeX: Real-time View Synthesis with Neural Basis Expansion

Github: https://github.com/nex-mpi/nex-code

Real-time View Synthesis with Python Code: https://analyticsindiamag.com/what-is-nex-guide-to-real-time-view-synthesis-with-python-code/

Project: https://nex-mpi.github.io/

Paper: https://arxiv.org/pdf/2103.05606.pdf

Video: https://www.youtube.com/watch?v=HyfkF7Z-ddA&ab_channel=SupasornSuwajanakorn

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EasyMocap is an open-source toolbox for markerless human motion capture from RGB videos

Github: https://github.com/zju3dv/EasyMocap

Paper: https://arxiv.org/abs/2104.00340v1

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👁 Learning Spatio-Temporal Transformer for Visual Tracking

Github: https://github.com/researchmm/Stark

Paper: https://arxiv.org/abs/2103.17154v1

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Audio-Visual Event Localization in Unconstrained Videos

https://sites.google.com/view/audiovisualresearch

Github: https://github.com/YapengTian/AVE-ECCV18

Paper: https://arxiv.org/abs/1803.08842

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Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers

Github: https://github.com/speechbrain/speechbrain/tree/develop/recipes/timers-and-such

Dataset: https://zenodo.org/record/4623772#.YG08zhQzZs9

Paper: https://arxiv.org/abs/2104.01604v1

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Regularizing Generative Adversarial Networks under Limited Data

Github: https://github.com/google/lecam-gan

Paper: https://arxiv.org/abs/2104.03310

https://hytseng0509.github.io/lecam-gan/

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📗 New updates: Interpretable Machine Learning

A Guide for Making Black Box Models Explainable.

https://christophm.github.io/interpretable-ml-book/

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🦏 DoubleML - Double Machine Learning in Python

Github: https://github.com/DoubleML/doubleml-for-py

Paper: https://arxiv.org/abs/2104.03220v1

Docs: https://docs.doubleml.org/stable/index.html

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💬 New Facebook's Casual Conversations Dataset over 45,000 videos

Dataset: https://ai.facebook.com/datasets/casual-conversations-dataset/

Paper: https://ai.facebook.com/blog/shedding-light-on-fairness-in-ai-with-a-new-data-set/

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Implicit Neural Representations with Periodic Activation Functions

Github: https://github.com/lucidrains/siren-pytorch

Paper: https://arxiv.org/abs/2006.09661

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PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks

Github: https://github.com/PaddlePaddle/PaddleGAN

Paper: https://arxiv.org/abs/2104.05376v1

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🧠 Lite-HRNet: A Lightweight High-Resolution Network

Github: https://github.com/HRNet/Lite-HRNet

Paper: https://arxiv.org/abs/2104.06403

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