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Ian Walker

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2 papers
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2

ICML Conference 2019 Conference Paper

Graph Convolutional Gaussian Processes

  • Ian Walker
  • Ben Glocker

We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be applied to problems in machine learning for which the input observations are functions with domains on general graphs. The structure of these models allows for high dimensional inputs while retaining expressibility, as is the case with convolutional neural networks. We present applications of graph convolutional Gaussian processes to images and triangular meshes, demonstrating their versatility and effectiveness, comparing favorably to existing methods, despite being relatively simple models.

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ICML Conference 2018 Conference Paper

Semi-Supervised Learning via Compact Latent Space Clustering

  • Konstantinos Kamnitsas
  • Daniel Coelho de Castro
  • Loïc Le Folgoc
  • Ian Walker
  • Ryutaro Tanno
  • Daniel Rueckert
  • Ben Glocker
  • Antonio Criminisi

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.

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