@Machine_learn

Fresh picks from ArXiv
This week is full of CVPR and AISTATS 20 accepted papers, new surveys, more submissions to ICML and KDD, and new GNN models 📚
@Machine_learn
CVPR 20
* Unbiased Scene Graph Generation from Biased Training
* Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
* 4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras
* Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs
* Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs
* Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
* SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks
@Machine_learn
Survey
* Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
* Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
* Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study
* Knowledge Graphs on the Web -- an Overview
@Machine_learn
GNN
* Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes
* Can graph neural networks count substructures? by group of Joan Bruna
* Heterogeneous Graph Neural Networks for Malicious Account Detection by group of Le Song
@Machine_learn
AISTATS 20
* Permutation Invariant Graph Generation via Score-Based Generative Modeling
@Machine_learn
KDD 20
* PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
@Machine_learn
ICML 20
* Semi-supervised Anomaly Detection on Attributed Graphs
* Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data
* Permutohedral-GCN: Graph Convolutional Networks with Global Attention
@Machine_learn
Graph Theory
* Finding large matchings in 1-planar graphs of minimum degree 3
* Trapping problem on star-type graphs with applications
* On Fast Computation of Directed Graph Laplacian Pseudo-Inverse

Learn Keras for Deep Neural Networks — Jojo Moolayil (en) 2019.
#middle #book #keras
@Machine_learn

Learn Keras for Deep Neural Networks — Jojo Moolayil (en) 2019.
#middle #book #keras
@Machine_learn

@Machine_learn

Deep learning of dynamical attractors from time series measurements

Code: https://github.com/williamgilpin/fnn

Paper: https://arxiv.org/abs/2002.05909

@Machine_learn

More than 200 NLP datasets - this is gold (last update 21.01.202)

https://quantumstat.com/dataset/dataset.html

and also Google provided dataset search tool for publicly available datasets:

https://datasetsearch.research.google.com/

سلام دوستان برای یه کار تحقیق نیاز به یسری دیتاست در زمینه تحلیل احساس فارسی داریم (به غیر از توییتر) ممنون میشم اگر کسی داره در پیوی برای بنده به اشتراک بزاره

@raminmousa

@Machine_learn
MARKOV CHAIN MONTE CARLO (MCMC) SAMPLING

https://www.tweag.io/posts/2019-10-25-mcmc-intro1.html

Habr ru: https://habr.com/ru/company/piter/blog/491268/

Artificial Intelligence Forecasting of Covid-19 in China
#paper
#Corona_virus
@Machine_learn

@Machine_learn
#XGBoost
XGBoost: An Intuitive Explanation
Ashutosh Nayak :
https://towardsdatascience.com/xgboost-an-intuitive-explanation-88eb32a48eff

#Corona_virus

#Iran🤒
@Machine_learn

Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning

@Machine_learn


https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html

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/