Fresh picks from ArXiv
This week is accepted papers to CVPR and WebConf, submissions to ICML, 130-page survey on knowledge graphs and algorithms for rainbow vertex coloring 🌈
@Machine_learn
CVPR 20
* Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
* Bundle Adjustment on a Graph Processor
@Machine_learn
WebConf 20
* Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
* Heterogeneous Graph Transformer
* Learning to Hash with Graph Neural Networks for Recommender Systems
@Machine_learn
ICML 20
* Neural Enhanced Belief Propagation on Factor Graphs by group of Max Welling
@Machine_learn
Survey
* A Survey on The Expressive Power of Graph Neural Networks
@Machine_learn
by Ryoma Sato
* A Survey on Deep Hashing Methods
* Knowledge Graphs
* Knowledge Graphs and Knowledge Networks: The Story in Brief
@Machine_learn
Graph Theory
* Properties of Erdős-Rényi Graphs
* Algorithms for the rainbow vertex coloring problem on graph classes
* Direct Product Primality Testing of Graphs is GI-hard

1.Generative Adversarial Networks with python by Jason Brownlee
2.imbalanced classification with python by Jason Brownlee

I want these two books

@Raminmousa

Generative Adversarial Networks with python by Jason Brownlee #book and #code @Machine_learn

@machine_learn
A Survey on The Expressive Power of Graph Neural Networks

This is the best survey on the theory on GNNs I'm aware of. It produces so many illustrative examples on what GNN can and cannot distinguish.

It's funny, it's made by Ryoma Sato who I already saw from other works on GNNs and I thought it's one of these old Japanese professors with long beard and strict habits, but it turned out to be a 1st year MSc student 🇯🇵

Jason Brownlee
Machine Learning Mastery With Python
#book #python
@Machine_learn

"Deep learning for Computer Vision by Jason brownlee"

Please share it with me
@raminmousa
https://machinelearningmastery.com/deep-learning-for-computer-vision/

Clever Algorithms
#book #AI
@Machine_learn

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@Machine_learn
MaxUp: A Simple Way to Improve Generalization of Neural Network Training

A new approach to augmentation both images and text. The idea is to generate a set of augmented data with some random perturbations or transforms and minimize the maximum, or worst case loss over the augmented data. By doing so, the authors implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance. Testing MaxUp on a range of tasks, including image classification, language modeling, and adversarial certification, it is consistently outperforming the existing best baseline methods, without introducing substantial computational overhead.
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paper: https://arxiv.org/abs/2002.09024

#augmentations #SOTA #ml

@Machine_learn

Paper:
https://arxiv.org/pdf/2003.05534.pdf

Github:
https://github.com/sniklaus/softmax-splatting

Short Summary:
https://www.marktechpost.com/2020/03/14/softmax-splatting-for-video-frame-interpolation/

Learning Pandas
#book #Python #Pandas
@Machine_learn

@Machine_learn


Fast and Easy Infinitely Wide Networks with Neural Tangents


https://ai.googleblog.com/2020/03/fast-and-easy-infinitely-wide-networks.html

Colab notebook: https://colab.research.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb#scrollTo=Lt74vgCVNN2b

Code: https://github.com/google/neural-tangents

Paper: https://arxiv.org/abs/1912.02803

@Machine_learn

Meta-Transfer Learning for Zero-Shot Super-Resolution

Code: https://github.com/JWSoh/MZSR

Paper: https://arxiv.org/abs/2002.12213v1

A new paper from Samsung AI Center (Moscow) on unpaired image-to-image translation. Now – without any domain labels, even on training time!
▶️ youtu.be/DALQYKt-GJc
📝 arxiv.org/abs/2003.08791
📉 @Machine_learn

seaborn tutorial
#book #python @Machine_learn

@Machine_learn


Anomaly detection with Keras, TensorFlow, and Deep Learning

In this tutorial, you will learn how to perform anomaly and outlier detection using autoencoders, Keras, and TensorFlow.

https://www.pyimagesearch.com/2020/03/02/anomaly-detection-with-keras-tensorflow-and-deep-learning/

@Machine_learn

Graph Machine Learning research groups: Le Song

Le Song (~1981)
- Affiliation: Georgia Institute of Technology;
- Education: Ph.D. at U. of Sydney in 2008 (supervised by Alex Smola);
- h-index: 59;
- Awards: best papers at ICML, NeurIPS, AISTATS;
- Interests: generative and adversarial graph models, social network analysis, diffusion models.

@Machine_learn

Rethinking Image Mixture for Unsupervised Visual Representation Learning

Code: https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning

Paper: https://arxiv.org/abs/2003.05438v1