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🔍 **Problem Description**:
🧠 **Model Description**:
The project will implement a **Convolutional Neural Network (CNN)** using **Transfer Learning** with a pre-trained model such as **ResNet50** …
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The field of graph representation learning has grown at an incredible and sometimes unwieldy pace over the past few years, and a lot of new algorithms and innovations were made in the field. I read th…
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Pose a question about one of the following possibility readings: “[Reducing the Dimensionality of Data with Neural Networks](https://science.sciencemag.org/content/313/5786/504.full)”, Hinton, G. E., …
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This is a dgl implementation for the paper 'Relational inductive biases, deep learning, and graph networks (https://arxiv.org/pdf/1806.01261.pdf)'. A simple example about node/edge/global feature upda…
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Hello,
As we talked before about adding Spatio-Temporal GNN models to PyG. I suggest papers that I mentioned below for start. Please take a look at them.
1-[Structured Sequence Modeling with Gr…
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Hi Thomas,
I am new in graph neural network field and I was going through different kinds of graph networks proposed by you. I want to solve very simple problem using graph networks. The problem lo…
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# Universality of Neural Networks on Sets vs. Graphs | ICLR Blogposts 2023
Universal function approximation is one of the central tenets in theoretical deep learning research. It is the question of w…
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I think an updated readme will help to see how different components are connected, even if the structure of the project might change. I'll volunteer to do this since it will help me get a better sense…
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**Dynamic neural network** is an emerging technology in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynamic networks can adapt…
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Some more classic ML examples that might be interesting:
- [ ] Graph Neural Networks (message passing) with the sparse indices
- [ ] Optimizers (ADAM / AdaGrad, not sure if they are interesting t…
srush updated
3 years ago