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A domain agnostic measure for monitoring and evaluating GANs
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi +4
Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential f…
Hyperbolic Neural Networks
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of…
A Distributed Second-Order Algorithm You Can Trust
Celestine Dünner, Aurelien Lucchi, Matilde Gargiani +3
Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate…
Deep State Space Models for Unconditional Word Generation
Florian Schmidt, Thomas Hofmann
Autoregressive feedback is considered a necessity for successful unconditional text generation using stochastic sequence models. However, such feedback is known to introduce system…
Local Saddle Point Optimization: A Curvature Exploitation Approach
Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi +1
Gradient-based optimization methods are the most popular choice for finding local optima for classical minimization and saddle point problems. Here, we highlight a systemic issue o…
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann
Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning. We here present a nov…