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Amortized Probabilistic Detection of Communities in Graphs
Yueqi Wang, Yoonho Lee, Pallab Basu +4
Learning community structures in graphs has broad applications across scientific domains. While graph neural networks (GNNs) have been successful in encoding graph structures, exis…
Bayesian Deep Ensembles via the Neural Tangent Kernel
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dyna…
Non-exchangeable feature allocation models with sublinear growth of the feature sizes
Giuseppe Di Benedetto, François Caron, Yee Whye Teh
Feature allocation models are popular models used in different applications such as unsupervised learning or network modeling. In particular, the Indian buffet process is a flexibl…
Robust Pruning at Initialization
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet +1
Overparameterized Neural Networks (NN) display state-of-the-art performance. However, there is a growing need for smaller, energy-efficient, neural networks tobe able to use machin…
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Umut Şimşekli, Lingjiong Zhu, Yee Whye Teh +1
Stochastic gradient descent with momentum (SGDm) is one of the most popular optimization algorithms in deep learning. While there is a rich theory of SGDm for convex problems, the…