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https://arxiv.org/pdf/1906.01629
```bib
@misc{gasse2019exactcombinatorialoptimizationgraph,
title={Exact Combinatorial Optimization with Graph Convolutional Neural Networks},
author…
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Given the recent popularity of graph convolutional neural networks (i.e. https://github.com/tkipf/gcn), would it be worth implementing a swift paradigmatic version? I have a semi-working version, but …
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Suggested list of courses would be:
- An introduction to deep learning **
- How to train a neural network
- Regularisation in neural networks
- Deep Bayesian neural networks
- Conv…
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I am very interested in your research _Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction_, and _Integrated Brain Connectivity Analysis with fMRI, D…
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Hey John! Here's the curriculum that I've worked on in the past. It's a bit less focused on language models as a sole topic, and more on modern ML from a broad perspective.
- Essential Concepts of …
zmaas updated
3 months ago
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From [Algorithmic Simplicity](https://www.youtube.com/@algorithmicsimplicity):
- [x] [Why Does Diffusion Work Better than Auto-Regression? - YouTube](https://www.youtube.com/watch?v=zc5NTeJbk-k)
-…
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## 一言でいうと
Graph Convolutionをプロダクションレベル(30億ノード！)で使用したという金字塔的な論文(Pinterestで使われている)。グラフサイズが膨大なので近傍ノードをサンプリング(ランダムウォーク)で収集しており、収集はCPU・畳み込みはGPUで役割分担しMapReduceで分散処理して高速化している。
### 論文リンク
https://arxi…
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Please add explicit support for graph-structured data including
- quantum graph kernels
- quantum graph neural networks, particularly quantum graph convolutional networks
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readme is a little bit simple.
I just started learning about graph neural networks,so i want to run through you code then learn your paper.
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Read the papers in [Deep Learning’s Most Important Ideas](https://www.kdnuggets.com/2020/09/deep-learnings-most-important-ideas.html).
- [x] Tackling ImageNet with AlexNet and Dropout
- [x] [I…