Auto-Encoding Variational Bayes
A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
Semi-Supervised Classification with Graph Convolutional Networks
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.
Modeling Relational Data with Graph Convolutional Networks
- M. Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, M. Welling
- Computer ScienceExtended Semantic Web Conference
- 17 March 2017
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.
Variational Graph Auto-Encoders
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.
Bayesian Learning via Stochastic Gradient Langevin Dynamics
In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic…
Semi-supervised Learning with Deep Generative Models
It is shown that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.
Improved Variational Inference with Inverse Autoregressive Flow
A new type of normalizing flow, inverse autoregressive flow (IAF), is proposed that, in contrast to earlier published flows, scales well to high-dimensional latent spaces and significantly improves upon diagonal Gaussian approximate posteriors.
Group Equivariant Convolutional Networks
Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries and achieves state of the art results on CI- FAR10 and rotated MNIST.
Attention-based Deep Multiple Instance Learning
- Maximilian Ilse, Jakub M. Tomczak, M. Welling
- Computer ScienceInternational Conference on Machine Learning
- 13 February 2018
This paper proposes a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism that achieves comparable performance to the best MIL methods on benchmark MIL datasets and outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.
Graph Convolutional Matrix Completion
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.