2 citations · 2 across the 4 of their papers we have counts for
8 papers
Learned transform compression with optimized entropy encoding
Magda Gregorová, Marc Desaules, Alexandros Kalousis
We consider the problem of learned transform compression where we learn both, the transform as well as the probability distribution over the discrete codes. We utilize a soft relax…
Improving VAE generations of multimodal data through data-dependent conditional priors
Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. Thi…
Sparse Learning for Variable Selection with Structures and Nonlinearities
Magda Gregorova
In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity…
Continual Classification Learning Using Generative Models
Frantzeska Lavda, Jason Ramapuram, Magda Gregorova +1
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple task…
Structured nonlinear variable selection
Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs. We focus on general nonlinear functions…
Large-scale Nonlinear Variable Selection via Kernel Random Features
Magda Gregorová, Jason Ramapuram, Alexandros Kalousis +1
We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, no…