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Semi-Supervised Classification with Graph Convolutional Networks
TLDR
A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin. Expand
Variational Graph Auto-Encoders
TLDR
The variational graph auto-encoder (VGAE) is introduced, a framework for unsupervised learning on graph-structured data based on the variational auto- Encoder (VAE) that can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets. Expand
Modeling Relational Data with Graph Convolutional Networks
TLDR
It is shown that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline. Expand
Graph Convolutional Matrix Completion
TLDR
A graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph that shows competitive performance on standard collaborative filtering benchmarks and outperforms recent state-of-the-art methods. Expand
Neural Relational Inference for Interacting Systems
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which can often beExpand
Hyperspherical Variational Auto-Encoders
TLDR
This work proposes using a von Mises-Fisher distribution instead of a Gaussian distribution for both the prior and posterior of the Variational Auto-Encoder, leading to a hyperspherical latent space. Expand
Learned Cardinalities: Estimating Correlated Joins with Deep Learning
TLDR
This work describes a new deep learning approach to cardinality estimation that builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Expand
MolGAN: An implicit generative model for small molecular graphs
TLDR
MolGAN is introduced, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuris-tics of previous likelihood-based methods. Expand
Object-Centric Learning with Slot Attention
TLDR
An architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention is presented. Expand
Contrastive Learning of Structured World Models
TLDR
These experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations. Expand
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