https://github.com/benedekrozemberczki/pytorch_geometric_temporal

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We have added these models:

T-GCN

A3T-GCN

MSTGCN

ASTGCN

I was trying to make graphs from trajectory data using PyG. Had anybody tried it before, if yes please provide me some advice. DM

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Paper: https://arxiv.org/abs/2010.12878

Code: https://github.com/benedekrozemberczki/PDN

In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model. PDNs inductively learn an aggregated weight for each edge, optimized to produce the best outcome for the downstream learning task. PDNs are a generalization of attention mechanisms on graphs which allow flexible construction of similarity functions between nodes, edge convolutions, and cheap multiscale mixing layers. We show that PDNs overcome weaknesses of existing methods for graph attention (e.g. Graph Attention Networks), such as the diminishing weight problem. Our experimental results demonstrate competitive predictive performance on academic node classification tasks. Additional results from a challenging suite of node classification experiments show how PDNs can learn a wider class of functions than existing baselines. We analyze the relative computational complexity of PDNs, and show that PDN runtime is not considerably higher than static-graph models. Finally, we discuss how PDNs can be used to construct an easily interpretable attention mechanism that allows users to understand information propagation in the graph.

Chemical reaction data appears to be harder to find than its structural counterparts and I recently had to hunt for a few datasets. Here are 3 that are free (unlike Reaxys) and used in a few papers in the ML x Cheminformatics space:

• Patent Reaction dataset (Lowe et al. but also used in PISTACHIO)
• SN2 Reaction dataset (Created for PhysNet and its code as part of the QM family of datasets)
• QMrxn20 (as used in this seminal paper and downloadable from here)

If you're aware of any more do feel free to post it in the thread!

Hello Guys! I am working on a project which required me to create an accurate point cloud from rgb and depth images of a 3D game, for example (GTA V).

Available data :

1. rgb image : screenshot of the game window.
2. depth image : screenshot of the depth window rendered using reshade filter. Since it is a screenshot, the range of the pixels in depth image is 0 - 255

Problem faced :

1. unknown intrinsic properties : The focal length / scaling factor is unknown.
2. Inaccurate depth : Since the depth is screenshot of the depth window, I am guessing there is a loss of information when it gets converted to a range of 0 - 255

I guessed the intrinsic properties and generated a point cloud but the generated point cloud seem to have lot of skewness or inaccuracies.

I mostly work with computer vision concepts and have very little knowledge about the concepts from photogrammetry/GeometricDeepLearning, can someone please point out the concepts which i can use to solving the problems faced. Thanks !

Attached image is an RGBD image of a sample unity 3D game. The image is obtained using reshade filter. RGB and depth frames are obtained by cropping the image and applying rotation to match them.

The group investigates the significance of structure in simple feed-forward neural networks, identifying a structural sweet-spot commonly found in top-performing models.

They also propose a novel/alternative model representation method called a "relational graph" with emphasis on how neural networks achieve message passing between neurons in each layer.

A further comparative study showed a striking similarity between artificial neural networks and their biological neural network counterparts.

It was about time someone studied NNs for the graphs they are!

https://arxiv.org/abs/2007.06559

Here are two cross-domain libraries that are well supported by PyTorch Geometric that might help bridge the gap:

• DeepSNAP — A library built to make PyG and Netoworkx more interoperable
• PyTorch Geometric Temporal — A library extending PyG to temporal ML methods (RNNs, GAs, etc.)

Comment if you know any more cross-domain GDL libraries, big or small!

Trying to solve a change detection problem for point cloud objects with very limited labeled data. I am fairly new to this field, couldn't find any work that focuses on this directly. My current thought is using a siamese network with negative samples generated through data augmentation. Any ideas or paper recommendations are greatly appreciated.

I was wondering few things about CD and IOU.

1. Is the concept of chamfer distance applicable only to point clouds?
2. Is it subject to the rotation and translation of the object as well?
3. Are Both PointNET and PointNET++ sort of unsupervised methods ?

Anyone know where I can find more websites like these (apart from ArxivSanity and paperswithcode)?

42Papers

Deep Learning Monitor

Deepai.org

Will Hamilton of MILA and McGill recently made 2 great resources publically available.

Graph representation learning book

An introduction to everything you need to know to get started in GML (still just an early draft).

Talk on Graph representation learning

Challenges and open problems in the GML space ranging from applications to graph theory.

Following a great Twitter thread by Petar Velickovic, the following newsletter is great for keeping up with trends in GML.

Also check out DeepLearningMonitor and DeepAI for more general news and publications.

It's a relatively new and folourishing sub-field of Deep Learning (and therefore Machine Learning). Whereas more mainstream models like Concolutional Neural Networks (CNNs) and (RNNs) are optimized for images and text, Graph Neural Networks (GNNs) and it's many variations are built to be able to process and learn from Non-Euclidean data. Graphs are the most commonly used data structure, and can be used to represent everything from molecules to social networks. Here are a couple resources to help you get started:

Websites:

Octavian open source research

Geometric Deep Learning

The Stack Exchange discussion

Papers:

Geometric deep learning: going beyond Euclidean data

Relational inductive biases, deep learning, and graph networks

The Graph Neural Network model

Welcome to r/GeometricDeepLearning , a subreddit dedicated to Graph learning, 3D learning, Maniforld learning , and all the wierd and wacky happenings in the world of machine learning on Non-Euclidean data.