28 citations · 177 across the 28 of their papers we have counts for
13 papers · 1 filter
LOREN: Low Rank-Based Code-Rate Adaptation in Neural Receivers
Bram Van Bolderik, Vlado Menkovski, Sonia Heemstra de Groot +1
Neural network based receivers have recently demonstrated superior system-level performance compared to traditional receivers. However, their practicality is limited by high memory…
Comparison of neural closure models for discretised PDEs
Hugo Melchers, Daan Crommelin, Barry Koren +2
Neural closure models have recently been proposed as a method for efficiently approximating small scales in multiscale systems with neural networks. The choice of loss function and…
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Lu Yin, Vlado Menkovski, Meng Fang +5
Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically…
Calibrated Adversarial Training
Tianjin Huang, Vlado Menkovski, Yulong Pei +1
Adversarial training is an approach of increasing the robustness of models to adversarial attacks by including adversarial examples in the training set. One major challenge of prod…
Process Discovery Using Graph Neural Networks
Dominique Sommers, Vlado Menkovski, Dirk Fahland
Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph…
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs
Yoeri Poels, Vlado Menkovski
The goal of a classification model is to assign the correct labels to data. In most cases, this data is not fully described by the given set of labels. Often a rich set of meaningf…
On Generalization of Graph Autoencoders with Adversarial Training
Tianjin Huang, Yulong Pei, Vlado Menkovski +1
Adversarial training is an approach for increasing model's resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature rep…
A Metric for Linear Symmetry-Based Disentanglement
Luis A. Pérez Rey, Loek Tonnaer, Vlado Menkovski +2
The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) outlines the properties that should characterize a disentangled representation tha…
Explaining Predictions by Approximating the Local Decision Boundary
Georgios Vlassopoulos, Tim van Erven, Henry Brighton +1
Constructing accurate model-agnostic explanations for opaque machine learning models remains a challenging task. Classification models for high-dimensional data, like images, are o…
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
Lu Yin, Vlado Menkovski, Mykola Pechenizkiy
Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the…
Causal Discovery from Incomplete Data: A Deep Learning Approach
Yuhao Wang, Vlado Menkovski, Hao Wang +2
As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs. By encod…
Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning
Niels Hellinga, Vlado Menkovski
Many tasks such as retrieval and recommendations can significantly benefit from structuring the data, commonly in a hierarchical way. To achieve this through annotations of high di…
Anomaly Detection for imbalanced datasets with Deep Generative Models
Nazly Rocio Santos Buitrago, Loek Tonnaer, Vlado Menkovski +1
Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive…