79 citations · 106 across the 10 of their papers we have counts for
15 papers
Enabling Uncertainty Estimation in Iterative Neural Networks
Nikita Durasov, Doruk Oner, Jonathan Donier +2
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that…
DEBOSH: Deep Bayesian Shape Optimization
Nikita Durasov, Artem Lukoyanov, Jonathan Donier +1
Graph Neural Networks (GNNs) can predict the performance of an industrial design quickly and accurately and be used to optimize its shape effectively. However, to fully explore the…
HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation
Subeesh Vasu, Nicolas Talabot, Artem Lukoianov +3
Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives a…
The universality of skipping behaviours on music streaming platforms
Jonathan Donier
A recent study of skipping behaviour on music streaming platforms has shown that the skip profile for a given song -- i.e. the measure of the skipping rate as a function of the tim…
Ensemble-based cover song detection
Marc Sarfati, Anthony Hu, Jonathan Donier
Audio-based cover song detection has received much attention in the MIR community in the recent years. To date, the most popular formulation of the problem has been to compare the…
Scaling up deep neural networks: a capacity allocation perspective
Jonathan Donier
Following the recent work on capacity allocation, we formulate the conjecture that the shattering problem in deep neural networks can only be avoided if the capacity propagation th…