139 citations · 245 across the 9 of their papers we have counts for
11 papers
Meta-Learning with Shared Amortized Variational Inference
Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari
We propose a novel amortized variational inference scheme for an empirical Bayes meta-learning model, where model parameters are treated as latent variables. We learn the prior dis…
Discrete Point Flow Networks for Efficient Point Cloud Generation
Roman Klokov, Edmond Boyer, Jakob Verbeek
Generative models have proven effective at modeling 3D shapes and their statistical variations. In this paper we investigate their application to point clouds, a 3D shape represent…
Efficient Wait-k Models for Simultaneous Machine Translation
Maha Elbayad, Laurent Besacier, Jakob Verbeek
Simultaneous machine translation consists in starting output generation before the entire input sequence is available. Wait-k decoders offer a simple but efficient approach for thi…
Anytime Inference with Distilled Hierarchical Neural Ensembles
Adria Ruiz, Jakob Verbeek
Inference in deep neural networks can be computationally expensive, and networks capable of anytime inference are important in mscenarios where the amount of compute or quantity of…
Hierarchical Scene Coordinate Classification and Regression for Visual Localization
Xiaotian Li, Shuzhe Wang, Yi Zhao +2
Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local d…
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image
Roman Klokov, Jakob Verbeek, Edmond Boyer
We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image. Several approaches in this direction have been investigated that expl…