research
∙
07/21/2020
Bloom Origami Assays: Practical Group Testing
We study the problem usually referred to as group testing in the context...
share
research
∙
06/08/2020
Optimal Transport Graph Neural Networks
Current graph neural network (GNN) architectures naively average or sum ...
research
∙
05/13/2020
Crackovid: Optimizing Group Testing
We study the problem usually referred to as group testing in the context...
research
∙
02/20/2020
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Representing graphs as sets of node embeddings in certain curved Riemann...
research
∙
11/19/2019
Mixed-curvature Variational Autoencoders
It has been shown that using geometric spaces with non-zero curvature in...
research
∙
11/12/2019
Constant Curvature Graph Convolutional Networks
Interest has been rising lately towards methods representing data in non...
research
∙
07/23/2019
Noise Contrastive Variational Autoencoders
We take steps towards understanding the "posterior collapse (PC)" diffic...
research
∙
02/21/2019
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
The softmax function on top of a final linear layer is the de facto meth...
research
∙
10/15/2018
Poincaré GloVe: Hyperbolic Word Embeddings
Words are not created equal. In fact, they form an aristocratic graph wi...
research
∙
10/01/2018
Riemannian Adaptive Optimization Methods
Several first order stochastic optimization methods commonly used in the...
research
∙
05/23/2018
Hyperbolic Neural Networks
Hyperbolic spaces have recently gained momentum in the context of machin...
research
∙
04/03/2018
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Learning graph representations via low-dimensional embeddings that prese...
research
∙
10/31/2017