28 citations · 177 across the 28 of their papers we have counts for
31 papers
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…
Towards Learned Simulators for Cell Migration
Koen Minartz, Yoeri Poels, Vlado Menkovski
Simulators driven by deep learning are gaining popularity as a tool for efficiently emulating accurate but expensive numerical simulators. Successful applications of such neural si…
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…
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Lu Yin, Vlado Menkovski, Yulong Pei +1
Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a…
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…
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
Iftitahu Ni'mah, Meng Fang, Vlado Menkovski +1
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The re…
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…
Hierarchical Semantic Segmentation using Psychometric Learning
Lu Yin, Vlado Menkovski, Shiwei Liu +1
Assigning meaning to parts of image data is the goal of semantic image segmentation. Machine learning methods, specifically supervised learning is commonly used in a variety of tas…
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…
Plasma Confinement Mode Classification Using a Sequence-to-Sequence Neural Network With Attention
Francisco Matos, Vlado Menkovski, Alessandro Pau +2
In a typical fusion experiment, the plasma can have several possible confinement modes. At the TCV tokamak, aside from the Low (L) and High (H) confinement modes, an additional mod…
Bridging the Performance Gap between FGSM and PGD Adversarial Training
Tianjin Huang, Vlado Menkovski, Yulong Pei +1
Deep learning achieves state-of-the-art performance in many tasks but exposes to the underlying vulnerability against adversarial examples. Across existing defense techniques, adve…
Complex Vehicle Routing with Memory Augmented Neural Networks
Marijn van Knippenberg, Mike Holenderski, Vlado Menkovski
Complex real-life routing challenges can be modeled as variations of well-known combinatorial optimization problems. These routing problems have long been studied and are difficult…
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…
Pedestrian orientation dynamics from high-fidelity measurements
Joris Willems, Alessandro Corbetta, Vlado Menkovski +1
We investigate in real-life conditions and with very high accuracy the dynamics of body rotation, or yawing, of walking pedestrians - an highly complex task due to the wide variety…
Deep learning velocity signals allows to quantify turbulence intensity
Alessandro Corbetta, Vlado Menkovski, Roberto Benzi +1
Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically non-trivial fluctuations of the velocity field, over a wide range of len…
Classification of tokamak plasma confinement states with convolutional recurrent neural networks
F. Matos, V. Menkovski, F. Felici +2
During a tokamak discharge, the plasma can vary between different confinement regimes: Low (L), High (H) and, in some cases, a temporary (intermediate state), called Dithering (D).…
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation
Iftitahu Ni'mah, Vlado Menkovski, Mykola Pechenizkiy
This study mainly investigates two common decoding problems in neural keyphrase generation: sequence length bias and beam diversity. To tackle the problems, we introduce a beam sea…
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…
Micro-expression detection in long videos using optical flow and recurrent neural networks
Michiel Verburg, Vlado Menkovski
Facial micro-expressions are subtle and involuntary expressions that can reveal concealed emotions. Micro-expressions are an invaluable source of information in application domains…