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Found 15 papers, 8 papers with code
Optimal Transport Graph Neural Networks
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
Ranked #1 on
Graph Regression
on Lipophilicity
(using extra training data)
Crackovid: Optimizing Group Testing
We study the problem usually referred to as group testing in the context of COVID-19.
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases, e. g., hierarchical structures benefit from hyperbolic geometry.
Practical Accelerated Optimization on Riemannian Manifolds
We develop a new Riemannian descent algorithm with an accelerated rate of convergence.
Optimization and Control
Mixed-curvature Variational Autoencoders
Euclidean geometry has historically been the typical "workhorse" for machine learning applications due to its power and simplicity.
Constant Curvature Graph Convolutional Networks
Interest has been rising lately towards methods representing data in non-Euclidean spaces, e. g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e. g. scale-free, hierarchical or cyclical.
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization
We propose a novel second-order ODE as the continuous-time limit of a Riemannian accelerated gradient-based method on a manifold with curvature bounded from below.
Optimization and Control
Noise Contrastive Variational Autoencoders
We take steps towards understanding the "posterior collapse (PC)" difficulty in variational autoencoders (VAEs),~i. e.
Escaping Flat Areas via Function-Preserving Structural Network Modifications
We develop our method for fully-connected as well as convolutional layers.
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
The Softmax function on top of a final linear layer is the de facto method to output probability distributions in neural networks.
Riemannian Adaptive Optimization Methods
Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings.
Hyperbolic Neural Networks
However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning.
Ranked #1 on
Link Prediction
on WordNet
On the effect of pooling on the geometry of representations
In machine learning and neuroscience, certain computational structures and algorithms are known to yield disentangled representations without us understanding why, the most striking examples being perhaps convolutional neural networks and the ventral stream of the visual cortex in humans and primates.
