@Machine_learn


Learning to See Transparent Objects

ClearGrasp uses 3 neural networks: a network to estimate surface normals, one for occlusion boundaries (depth discontinuities), and one that masks transparent objects

Google research: https://ai.googleblog.com/2020/02/learning-to-see-transparent-objects.html

Code: https://github.com/Shreeyak/cleargrasp

Dataset: https://sites.google.com/view/transparent-objects

3D Shape Estimation of Transparent Objects for Manipulation: https://sites.google.com/view/cleargrasp

Reinforcement learning
#deeplearning #reinforcement
#python
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GANILLA: Generative Adversarial Networks for Image to Illustration Translation.

Github: https://github.com/giddyyupp/ganilla

Dataset: https://github.com/giddyyupp/ganilla/blob/master/docs/datasets.md

Paper: https://arxiv.org/abs/2002.05638v1

Deep learning of dynamical attractors from time series measurements

Embed complex time series using autoencoders and a loss function based on penalizing false-nearest-neighbors.

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

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

Deep Reinforcement Learning with Guar Performance
#DL #book #RL
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@Machine_learn
IBM Data Science and AI Programs Free for 30 Days

https://onlinecoursesgalore.com/ibm-data-science-ai-coursera/

Coursera: https://www.coursera.org/promo/ibmdscommunity?ranMID=40328&ranEAID

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Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer

https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html

Code: https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb

Github: https://github.com/google-research/text-to-text-transfer-transformer

https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset


#Corona_virus dataset

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Graph ML Surveys

A good way to start in this domain is to read what people already have done.

Videos
* Learning on Non-Euclidean Domains
* Stanford Course CS 224w
@Machine_learn
GNN
* Graph Neural Networks: A Review of Methods and Applications 2018
* A Comprehensive Survey on Graph Neural Networks 2019
* A Gentle Introduction to Deep Learning for Graphs 2019
* Deep Learning on Graphs: A Survey 2018
* Relational inductive biases, deep learning, and graph networks 2018
* Geometric deep learning: going beyond Euclidean data 2016
* Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview 2019
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Graph kernels
* A Survey on Graph Kernels 2019
* Graph Kernels: A Survey 2019
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Adversarial Attacks
* Adversarial Attack and Defense on Graph Data: A Survey 2018
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Representation Learning
* Learning Representations of Graph Data -- A Survey 2019
* Representation Learning on Graphs: Methods and Applications 2017
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@Machine_learn

Fresh picks from ArXiv
ICML 20 submissions, AISTATS 20, graphs in math, and Stephen Hawking 👨‍🔬

ICML 2020 submissions
Fast Detection of Maximum Common Subgraph via Deep Q-Learning (https://arxiv.org/abs/2002.03129)
Random Features Strengthen Graph Neural Networks (https://arxiv.org/abs/2002.03155)
Hierarchical Generation of Molecular Graphs using Structural Motifs (https://arxiv.org/pdf/2002.03230.pdf)
Graph Neural Distance Metric Learning with Graph-Bert (https://arxiv.org/abs/2002.03427)
Segmented Graph-Bert for Graph Instance Modeling (https://arxiv.org/abs/2002.03283)
Haar Graph Pooling (https://arxiv.org/abs/1909.11580)
Constant Time Graph Neural Networks (https://arxiv.org/abs/1901.07868)
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AISTATS 20
Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis (https://arxiv.org/abs/1907.05632)
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Math
Some arithmetical problems that are obtained by analyzing proofs and infinite graphs (https://arxiv.org/abs/2002.03075)
Extra pearls in graph theory (https://arxiv.org/abs/1812.06627)
Distance Metric Learning for Graph Structured Data (https://arxiv.org/abs/2002.00727)
@Machine_learn
Surveys
Generalized metric spaces. Relations with graphs, ordered sets and automata : A survey (https://arxiv.org/abs/2002.03019)
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Stephen Hawking 👨‍🔬
Stephen William Hawking: A Biographical Memoir (https://arxiv.org/abs/2002.03185)

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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
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AISTATS 20
* Permutation Invariant Graph Generation via Score-Based Generative Modeling
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KDD 20
* PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
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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
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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
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Learn Keras for Deep Neural Networks — Jojo Moolayil (en) 2019.
#middle #book #keras
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@Machine_learn

Deep learning of dynamical attractors from time series measurements

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

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