A Browsable Petascale Reconstruction of the Human Cortex


http://ai.googleblog.com/2021/06/a-browsable-petascale-reconstruction-of.html

@Machine_learn

Implementing original #UNet paper using #PyTorch

Video tutorial on how to code your own neural network from scratch.

Link: https://www.youtube.com/watch?v=u1loyDCoGbE&t=1s
Paper: https://arxiv.org/abs/1505.04597

@Machine_learn

A Tool Of Choice for Bootstrapping High Quality Python Packages

https://morioh.com/p/65db96717d00

The Demo/Documentation: https://pyscaffold.org/

Download Link: https://github.com/pyscaffold/pyscaffold/archive/refs/heads/master.zip

Official Website: https://github.com/pyscaffold/pyscaffold

@Machine_learn

Fresh picks from ArXiv
This week on ArXiv: 1000-layer GNN, solutions to OGB challenge, and theory behind GNN explanations 🤔

If I forgot to mention your paper, please shoot me a message and I will update the post.


Deep GNNs
* Training Graph Neural Networks with 1000 Layers ICML 2021
* Very Deep Graph Neural Networks Via Noise Regularisation with Petar Veličković, Peter Battaglia

Heterophily
* Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs with Danai Koutra

Knowledge graphs
* Query Embedding on Hyper-relational Knowledge Graphs with Mikhail Galkin

OGB-challenge
* Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
* First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track

Theory
* Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations with Marinka Zitnik
* A unifying point of view on expressive power of GNNs

GNNs
* Stability of Graph Convolutional Neural Networks to Stochastic Perturbations with Alejandro Ribeiro
* TD-GEN: Graph Generation With Tree Decomposition
* Unsupervised Resource Allocation with Graph Neural Networks
* Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
* GemNet: Universal Directional Graph Neural Networks for Molecules with Stephan Günnemann
* Optimizing Graph Transformer Networks with Graph-based Techniques

Survey
* Systematic comparison of graph embedding methods in practical tasks
* Evaluating Modules in Graph Contrastive Learning
* A Survey on Mining and Analysis of Uncertain Graphs

@Machine_learn

Facebook's Reverse engineering generative models from a single deepfake image

Github: https://github.com/vishal3477/Reverse_Engineering_GMs

Paper: https://arxiv.org/abs/2106.07873

Facebook's blog: https://ai.facebook.com/blog/reverse-engineering-generative-model-from-a-single-deepfake-image/

Dataset: https://drive.google.com/drive/folders/1ZKQ3t7_Hip9DO6uwljZL4rYAn5viSRhu?usp=sharing

@Machine_learn

Amazon, Berkeley release dataset of product images and metadata.
Dataset includes multiple images of 147,702 products, including 360° rotations and 3-D models for thousands of them.


https://www.amazon.science/blog/amazon-berkeley-release-dataset-of-product-images-and-metadata

@Machine_learn

Breast Cancer Wisconsin (Diagnostic) Data Set

Predict whether the cancer is benign or malignant

Here is link of dataset: Link

🔷 Number of instances: 569
🔷 Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)
🔷 Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)


#dataset
@Machine_learn

R and Python for oceanographers

⬇️ Book

@Machine_learn

Self-Supervised Learning with Swin Transformers

Github: https://github.com/SwinTransformer/Transformer-SSL

Paper: https://arxiv.org/abs/2105.04553v2

@Machine_learn

SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping

Paper:
https://arxiv.org/pdf/2105.07014.pdf

Video:
https://www.youtube.com/watch?v=W7NCbfZp6QE

Code:
https://github.com/google-research/google-research/tree/master/smurf

@Machine_learn

GIRAFFE: A Closer Look at the Code for CVPR 2021’s Best Paper

[Paper] http://www.cvlibs.net/publications/Niemeyer2021CVPR.pdf

[Source] https://github.com/autonomousvision/giraffe

[Blog] https://autonomousvision.github.io/giraffe/

[Interactive slides] https://m-niemeyer.github.io/slides/#/4

[Collected] https://m-niemeyer.github.io/project-pages/giraffe/index.html

@Machine_learn

Jason Brownlee
Master Machine Learning Algorithms Discover How They Work and Implement Them From Scratch
#Ml #book
@Machine_learn

EL-Attention: Memory Efficient Lossless Attention for Generation

Github: https://github.com/microsoft/fastseq

Paper: https://arxiv.org/abs/2105.04779v1

@Machine_learn

Deep Learning Dataset For Passage and Document Retrieval

Github: https://github.com/grill-lab/DL-Hard

Paper: https://arxiv.org/abs/2105.07975v1

@Machine_learn

Cartoon-StyleGan2 🙃 : Fine-tuning StyleGAN2 for Cartoon Face Generation

Github: https://github.com/happy-jihye/Cartoon-StyleGan2

Paper: https://arxiv.org/abs/2106.12445

Colab: https://colab.research.google.com/github/happy-jihye/Cartoon-StyleGan2/blob/main/Cartoon_StyleGAN2.ipynb

@Machine_learn

Neural Networks and Introduction to Deep Learning
#RNN #DL
@Machine_learn

🌠 Deepmind's Generally capable agents emerge from open-ended play

Blog : https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play

Paper: https://deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agents

DeepMind Research: https://github.com/deepmind/deepmind-research

Video: https://www.youtube.com/watch?v=lTmL7jwFfdw&ab_channel=DeepMind

@Machine_learn