There have been works on solving combinatorial problems with Graph Neural Networks. In this post, I will go through 3 important papers on this matter.
Table of Contents1. Overview
>>> What is a combinatorial problem?
>>> Why Graph Neural Network?2. Outline of papers
>>> Approximation Ratios of Graph Neural Networks for Combinatorial Problems, Sato et al., NIPS 2019.
>>> Exact Combinatorial Optimization with Graph Convolutional Neural Networks, Gasse et al., NIPS 2019.
>>> Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP, Prates et al., AAAI 2019. …
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For e-commerce and social media platforms, a recommender system is important. Motivation for GNN on RS: (1) RS data is essentially graphs:
Relevant information includes social relationships, bipartite user-item interaction graphs, item transitions, which can all be represented well as a graph structure. (2) GNN is good on graphs.
static preference. model user-item compatibility by implicit (click) or explicit (rating)
Can utilize side knowledge:
Transition pattern for next…
As documented on their website, ShapeNet is an ongoing collaborative effort between researchers at Princeton, Stanford, and TTIC to establish a richly annotated, large-scale dataset of 3D shapes. Currently, ShapeNetCore is a subset of ShapeNet containing single clean 3D models with manually verified category and alignment annotations are released.
In ShapeNetCore (v2), the directory structure is roughly:
> taxonomy.json: listing the synsetId(s) and the English name(s) of the model-type, as well as the combined number of model instances.
> [synsetId]: synset noun offset for the model type in WordNet v3.0 (v3.1 is available online) as an eight-digit zero-padded string, e.g…
Deep Learning Networks are prone to be messed with “Adversarial Attacks.” Now this problem has come to haunt Graph Neural Networks. In this post, 2 papers and a tutorial are summarized.
Table of Contents1. Overview
>>> What is an Adversarial Attack
>>> Attack on GNN2. Outlines
>>> Adversarial Attacks on Neural Networks for Graph Data, Zügner et al., KDD 2018.
>>> Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, Wu et al., IJCAI 2019.
>>> Deep Graph Learning: Foundations, Advances and Applications, Rong et al., KDD 2020 tutorial. (Robustness of GNN)
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Learning Category-Specific Mesh Reconstruction from Image Collections
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
DeepSFM: Structure From Motion Via Deep Bundle Adjustment
Deep Non-Rigid Structure from Motion
Deep nrsfm++: Towards 3d reconstruction in the wild
Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
Table of ContentWhat is mesh?Popular 3d model dataset collectionsTransformation: sampling point cloud from CAD or mesh
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In the last lecture, we find smoothness required for convergence guarantee. How to generalize smoothness? Ans: Bregman divergence.
no time to write will finish latter