
Optimal Transport Graph Neural Networks
Current graph neural network (GNN) architectures naively average or sum ...
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Crackovid: Optimizing Group Testing
We study the problem usually referred to as group testing in the context...
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Computationally Tractable Riemannian Manifolds for Graph Embeddings
Representing graphs as sets of node embeddings in certain curved Riemann...
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Mixedcurvature Variational Autoencoders
It has been shown that using geometric spaces with nonzero curvature in...
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Constant Curvature Graph Convolutional Networks
Interest has been rising lately towards methods representing data in non...
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Noise Contrastive Variational Autoencoders
We take steps towards understanding the "posterior collapse (PC)" diffic...
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Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Nonlinearities
The softmax function on top of a final linear layer is the de facto meth...
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Poincaré GloVe: Hyperbolic Word Embeddings
Words are not created equal. In fact, they form an aristocratic graph wi...
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Riemannian Adaptive Optimization Methods
Several first order stochastic optimization methods commonly used in the...
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Hyperbolic Neural Networks
Hyperbolic spaces have recently gained momentum in the context of machin...
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Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Learning graph representations via lowdimensional embeddings that prese...
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Parametrizing filters of a CNN with a GAN
It is commonly agreed that the use of relevant invariances as a good sta...
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