Depth-supervised NeRF: Fewer Views and Faster Training for Free

Github: https://github.com/dunbar12138/DSNeRF

Paper: https://arxiv.org/abs/2107.02791v1

Project: https://www.cs.cmu.edu/~dsnerf/

Datasets: https://paperswithcode.com/dataset/3dmatch

👉 @bigdata_1

Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation

Github: https://github.com/Elvinky/IEGAN

Paper: https://arxiv.org/abs/2107.02494

Datasets: https://github.com/Elvinky/IEGAN/tree/main/dataset

Image-to-Image Translation: https://paperswithcode.com/task/image-to-image-translation

👉 @bigdata_1

GraphWorld: бенчмарк для графовых нейросетей

https://ai.googleblog.com/2022/05/graphworld-advances-in-graph.html

👉 @bigdata_1

Highly accurate protein structure prediction with AlphaFold

https://deepmind.com/blog/article/putting-the-power-of-alphafold-into-the-worlds-hands

Github: https://github.com/deepmind/alphafold

Paper: https://doi.org/10.1038/s41586-021-03819-2

👉 @bigdata_1

GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior

Github: https://github.com/TencentARC/GFPGAN

Paper: https://arxiv.org/abs/2101.04061v2

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

Colab Demo: https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo

👉 @bigdata_1

Tackling multiple tasks with a single visual language model

https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model

👉 @bigdata_1

Как импортировать наборы данных Kaggle в Google Colab?

Kaggle используется практически всеми, кто занимается наукой о данных. Эта платформа содержит датасеты для всех областей знаний. Вы можете получить набор данных практически для любого случая использования, включая развлечения, медицину, электронную коммерцию и даже астрономию. Пользователи Kaggle могут попрактиковаться на различных наборах, чтобы проверить свои компетенции в науке о данных и машинном обучении.

В этой статье будет показано, как импортировать наборы данных из Kaggle непосредственно в блокноты Google Colab.

👉 @bigdata_1

🐍 PyTorch Fundamentals Free Microsoft Course

https://docs.microsoft.com/en-us/learn/paths/pytorch-fundamentals/

Ru: https://docs.microsoft.com/ru-ru/learn/paths/pytorch-fundamentals/

Github: https://github.com/pytorch/tutorials

👉 @bigdata_1

Deepmind's WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

This package provides tools to download the WikiGraphs dataset

Github: https://github.com/deepmind/deepmind-research/tree/master/wikigraphs

Paper: https://arxiv.org/abs/2107.09556v1

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

👉 @bigdata_1

Speeding up Scikit-Learn Model Training

https://www.kdnuggets.com/2021/03/speed-up-scikit-learn-model-training.html

👉 @bigdata_1

CycleMLP: A MLP-like Architecture for Dense Prediction

Github: https://github.com/ShoufaChen/CycleMLP

Paper: https://arxiv.org/abs/2107.10224

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

👉 @bigdata_1

Attention Mechanism Exploits Temporal Contexts: Real-time 3D Human Pose Reconstruction (CVPR 2020 Oral)

https://sites.google.com/a/udayton.edu/jshen1/cvpr2020

Github: https://github.com/lrxjason/Attention3DHumanPose

Paper: https://arxiv.org/abs/2103.03170v1

👉 @bigdata_1

The Best SOTA NLP Course is Free!

https://huggingface.co/course/chapter1

Dataset: https://github.com/huggingface/datasets

Colab: https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter1/section3.ipynb

👉 @bigdata_1

TorchDrift: drift detection for PyTorch

https://torchdrift.org/

Github: https://github.com/torchdrift/torchdrift/

Example: https://torchdrift.org/notebooks/drift_detection_on_images.html

👉 @bigdata_1

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Github: https://github.com/xinntao/Real-ESRGAN

Paper: https://arxiv.org/abs/2107.10833v1

How to Train Real-ESRGAN: https://github.com/xinntao/Real-ESRGAN/blob/master/Training.md

Colab Demo: https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo

👉 @bigdata_1

Improving Graph Convolutional Networks with Lessons from Transformers

https://blog.einstein.ai/improving-graph-networks-with-transformers/

👉 @bigdata_1

AAVAE: Augmentation-Augmented Variational Autoencoders

Github: https://github.com/gridai-labs/aavae

Paper: https://arxiv.org/abs/2107.12329v1

Dataset: https://paperswithcode.com/dataset/cifar-10

👉 @bigdata_1

Guide To GPyTorch: A Python Library For Gaussian Process Models

https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/

👉 @bigdata_1

RLQP: Accelerating Quadratic Optimization with RL

Github: https://github.com/berkeleyautomation/rlqp

Paper: https://arxiv.org/abs/2107.10833v1

👉 @bigdata_1

Know your data much faster with the new Sweetviz Python library

https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html

👉 @bigdata_1

Introducing Triton: Open-Source GPU Programming for Neural Networks

https://openai.com/blog/triton/

Github: https://github.com/openai/triton

Documents: https://triton-lang.org/

👉 @bigdata_1