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Articles

Topological Deep Learning: A Survey on Topological Neural Networks
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Exploring Neural Graphics Primitives
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Contrastive Representation Learning
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The dark side of Graph Neural Networks
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Beyond message passing: A physics-inspired paradigm for graph neural networks
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Generative Flow Networks
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TensorFlow Graph Neural Networks
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A Gentle Introduction to Graph Neural Networks
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An attempt at demystifying graph deep learning
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Hopfield Networks Is All You Need
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Graph theory, graph convolutional networks, knowledge graphs
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Why I'm Lukewarm on Graph Neural Networks
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Transformers Are Graph Neural Networks
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Graph Representations for Higher-Order Logic and Theorem Proving (2019)
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Gated Linear Networks
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Transformers Are Graph Neural Networks
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Graph Neural Networks – An Overview
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Wide Neural Networks of Any Architecture Are Gaussian Processes
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L2 Regularization and Batch Norm
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Innovations in Graph Representation Learning
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Language, trees, and geometry in neural networks
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An Explicitly Relational Neural Network Architecture
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Graph Matching Networks for Learning the Similarity of Graph Structured Objects
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Graph Convolutional Networks (2016)
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Graph Neural Networks: A Review of Methods and Applications
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One-Shot Training of Neural Networks Using Hypercube-Based Topological Coverings
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Bayesian Networks – An Introduction (2016)
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Capsule networks can overcome some shortcomings of CNNs
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Capsule Networks Explained
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Relational Reasoning with Neural Networks
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Self-Normalizing Neural Networks
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TensorFlow Fold: Deep Learning with Dynamic Computation Graphs
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Show HN: Graph Convolutional Networks – Intro to neural networks on graphs
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Stanford CS231n – Convolutional Neural Networks for Visual Recognition
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Transformer as a general purpose computer
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Kolmogorov-Arnold Networks
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The Geometry of Categorical and Hierarchical Concepts in Large Language Models
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Trying Kolmogorov-Arnold Networks in Practice
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Transformer Explainer
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Graph Language Models
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