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2020 – today
- 2024
[c48]Bram Van Bolderik, Souradip Sarkar, Vlado Menkovski, Sonia Heemstra, Manil Dev Gomony:
Agile Design-Space Exploration of Dynamic Layer-Skipping in Neural Receivers. DSD 2024: 122-128- [c47]Wouter W. L. Nuijten
, Vlado Menkovski:
Node Classification in Random Trees. IDA (1) 2024: 105-116 - [c46]Marko Petkovic, Pablo Romero-Marimon, Vlado Menkovski, Sofía Calero:
Equivariant Parameter Sharing for Porous Crystalline Materials. IDA (1) 2024: 129-140 - [c45]Simon Martinus Koop, Mark A. Peletier, Jacobus Willem Portegies, Vlado Menkovski:
Neural Langevin Dynamics: Towards Interpretable Neural Stochastic Differential Equations. NLDL 2024: 130-137 - [i50]Marko Petkovic, José Manuel Vicent-Luna, Vlado Menkovski, Sofía Calero:
Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites. CoRR abs/2403.12659 (2024) - [i49]Jonas Niederle, Simon M. Koop, Marc Pagès-Gallego, Vlado Menkovski:
VADA: a Data-Driven Simulator for Nanopore Sequencing. CoRR abs/2404.08722 (2024) - [i48]Fleur Hendriks, Vlado Menkovski, Martin Doskár, Marc G. D. Geers, Ondrej Rokos:
Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials. CoRR abs/2404.17365 (2024) - [i47]Pol Timmer, Koen Minartz, Vlado Menkovski:
Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale. CoRR abs/2405.16608 (2024) - [i46]Yoeri Poels, Koen Minartz, Harshit Bansal, Vlado Menkovski:
Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates. CoRR abs/2405.17260 (2024) - [i45]Mahefa Ratsisetraina Ravelonanosy, Vlado Menkovski, Jacobus W. Portegies:
Topological degree as a discrete diagnostic for disentanglement, with applications to the ΔVAE. CoRR abs/2409.01303 (2024) - 2023
- [j9]Hugo Melchers, Daan Crommelin, Barry Koren, Vlado Menkovski, Benjamin Sanderse:
Comparison of neural closure models for discretised PDEs. Comput. Math. Appl. 143: 94-107 (2023) - [j8]Dominique Sommers, Vlado Menkovski, Dirk Fahland:
Supervised learning of process discovery techniques using graph neural networks. Inf. Syst. 115: 102209 (2023) - [c44]Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy:
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. AAAI 2023: 10945-10953 - [c43]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist. ACL (1) 2023: 1240-1266 - [c42]Loek Tonnaer, Mike Holenderski, Vlado Menkovski:
Out-of-Distribution Generalisation with Symmetry-Based Disentangled Representations. IDA 2023: 433-445 - [c41]Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu:
Dynamic Sparsity Is Channel-Level Sparsity Learner. NeurIPS 2023 - [c40]Koen Minartz, Yoeri Poels, Simon M. Koop, Vlado Menkovski:
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics. NeurIPS 2023 - [c39]Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy:
Enhancing Adversarial Training via Reweighting Optimization Trajectory. ECML/PKDD (1) 2023: 113-130 - [i44]Marko Petkovic, Pablo Romero-Marimon, Vlado Menkovski, Sofía Calero:
Equivariant Networks for Porous Crystalline Materials. CoRR abs/2304.01628 (2023) - [i43]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist. CoRR abs/2305.08566 (2023) - [i42]Koen Minartz, Yoeri Poels, Simon M. Koop, Vlado Menkovski:
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics. CoRR abs/2305.14286 (2023) - [i41]Yoeri Poels, Gijs Derks, Egbert Westerhof, Koen Minartz, Sven Wiesen, Vlado Menkovski:
Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates. CoRR abs/2305.18944 (2023) - [i40]Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu:
Dynamic Sparsity Is Channel-Level Sparsity Learner. CoRR abs/2305.19454 (2023) - [i39]Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy:
Enhancing Adversarial Training via Reweighting Optimization Trajectory. CoRR abs/2306.14275 (2023) - [i38]Iftitahu Ni'mah, Samaneh Khoshrou, Vlado Menkovski, Mykola Pechenizkiy:
KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering. CoRR abs/2310.19650 (2023) - [i37]Wouter W. L. Nuijten, Vlado Menkovski:
Node classification in random trees. CoRR abs/2311.12167 (2023) - [i36]Marko Petkovic, Vlado Menkovski:
Description Generation using Variational Auto-Encoders for precursor microRNA. CoRR abs/2311.17970 (2023) - 2022
- [j7]Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy:
Direction-aggregated Attack for Transferable Adversarial Examples. ACM J. Emerg. Technol. Comput. Syst. 18(3): 60:1-60:22 (2022) - [c38]Loek Tonnaer, Luis Armando Pérez Rey, Vlado Menkovski, Mike Holenderski, Jim Portegies:
Quantifying and Learning Linear Symmetry-Based Disentanglement. ICML 2022: 21584-21608 - [c37]Yoeri Poels, Vlado Menkovski:
VAE-CE: Visual Contrastive Explanation Using Disentangled VAEs. IDA 2022: 237-250 - [c36]Stepan Veretennikov, Koen Minartz, Vlado Menkovski, Burcu Gumuscu, Jan de Boer:
Simulation of Scientific Experiments with Generative Models. IDA 2022: 341-353 - [c35]Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Semantic-Based Few-Shot Classification by Psychometric Learning. IDA 2022: 392-403 - [c34]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. LoG 2022: 8 - [c33]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks. ECML/PKDD (1) 2022: 225-241 - [c32]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy:
Superposing many tickets into one: A performance booster for sparse neural network training. UAI 2022: 2267-2277 - [i35]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu:
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. CoRR abs/2205.15322 (2022) - [i34]Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy:
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. CoRR abs/2208.10842 (2022) - [i33]Koen Minartz, Yoeri Poels, Vlado Menkovski:
Towards Learned Simulators for Cell Migration. CoRR abs/2210.01123 (2022) - [i32]Hugo Melchers, Daan Crommelin, Barry Koren, Vlado Menkovski, Benjamin Sanderse:
Comparison of neural closure models for discretised PDEs. CoRR abs/2210.14675 (2022) - [i31]Simon M. Koop, Mark A. Peletier, Jacobus W. Portegies, Vlado Menkovski:
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations. CoRR abs/2211.09537 (2022) - [i30]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. CoRR abs/2211.15335 (2022) - 2021
- [j6]Shiwei Liu, Iftitahu Ni'mah, Vlado Menkovski, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Efficient and effective training of sparse recurrent neural networks. Neural Comput. Appl. 33(15): 9625-9636 (2021) - [c31]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
calibrated adversarial training. ACML 2021: 626-641 - [c30]Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy:
Hierarchical Semantic Segmentation using Psychometric Learning. ACML 2021: 798-813 - [c29]Marijn van Knippenberg, Mike Holenderski, Vlado Menkovski:
Time-Constrained Multi-Agent Path Finding in Non-Lattice Graphs with Deep Reinforcement Learning. ACML 2021: 1317-1332 - [c28]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection. EMNLP (Findings) 2021: 1606-1617 - [c27]Dominique Sommers, Vlado Menkovski, Dirk Fahland:
Process Discovery Using Graph Neural Networks. ICPM 2021: 40-47 - [c26]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. ECML/PKDD (2) 2021: 367-382 - [i29]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks. CoRR abs/2104.07917 (2021) - [i28]Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy:
Direction-Aggregated Attack for Transferable Adversarial Examples. CoRR abs/2104.09172 (2021) - [i27]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. CoRR abs/2107.02658 (2021) - [i26]Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy:
Hierarchical Semantic Segmentation using Psychometric Learning. CoRR abs/2107.03212 (2021) - [i25]Yoeri Poels, Vlado Menkovski:
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs. CoRR abs/2108.09159 (2021) - [i24]Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy:
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection. CoRR abs/2108.12229 (2021) - [i23]Dominique Sommers, Vlado Menkovski, Dirk Fahland:
Process Discovery Using Graph Neural Networks. CoRR abs/2109.05835 (2021) - [i22]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Calibrated Adversarial Training. CoRR abs/2110.00623 (2021) - [i21]Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing. CoRR abs/2112.09201 (2021) - 2020
- [c25]Sander M. Boelders, Venkata Srikanth Nallanthighal, Vlado Menkovski, Aki Härmä:
Detection of Mild Dyspnea from Pairs of Speech Recordings. ICASSP 2020: 4102-4106 - [c24]Marijn van Knippenberg, Mike Holenderski, Vlado Menkovski:
Complex Vehicle Routing with Memory Augmented Neural Networks. ICPS 2020: 303-308 - [c23]Jeroen van Doorenmalen, Vlado Menkovski:
Evaluation of CNN Performance in Semantically Relevant Latent Spaces. IDA 2020: 145-157 - [c22]Luis A. Pérez Rey, Vlado Menkovski, Jim Portegies:
Diffusion Variational Autoencoders. IJCAI 2020: 2704-2710 - [c21]Lu Yin, Vlado Menkovski, Mykola Pechenizkiy:
Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing. ECML/PKDD (2) 2020: 154-169 - [i20]Joris Willems, Alessandro Corbetta, Vlado Menkovski, Federico Toschi:
Pedestrian orientation dynamics from high-fidelity measurements. CoRR abs/2001.04646 (2020) - [i19]Yuhao Wang, Vlado Menkovski, Hao Wang, Xin Du, Mykola Pechenizkiy:
Causal Discovery from Incomplete Data: A Deep Learning Approach. CoRR abs/2001.05343 (2020) - [i18]Lu Yin, Vlado Menkovski, Mykola Pechenizkiy:
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing. CoRR abs/2004.06353 (2020) - [i17]Georgios Vlassopoulos, Tim van Erven, Henry Brighton, Vlado Menkovski:
Explaining Predictions by Approximating the Local Decision Boundary. CoRR abs/2006.07985 (2020) - [i16]Marijn van Knippenberg, Mike Holenderski, Vlado Menkovski:
Complex Vehicle Routing with Memory Augmented Neural Networks. CoRR abs/2009.10520 (2020) - [i15]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Bridging the Performance Gap between FGSM and PGD Adversarial Training. CoRR abs/2011.05157 (2020) - [i14]Loek Tonnaer, Luis A. Pérez Rey, Vlado Menkovski, Mike Holenderski, Jacobus W. Portegies:
Quantifying and Learning Disentangled Representations with Limited Supervision. CoRR abs/2011.06070 (2020) - [i13]Luis A. Pérez Rey, Loek Tonnaer, Vlado Menkovski, Mike Holenderski, Jacobus W. Portegies:
A Metric for Linear Symmetry-Based Disentanglement. CoRR abs/2011.13306 (2020)
2010 – 2019
- 2019
- [c20]Hameed Abdul-Rashid, Juefei Yuan, Bo Li, Yijuan Lu, Tobias Schreck, Ngoc-Minh Bui, Trong-Le Do, Mike Holenderski, Dmitri Jarnikov, Tu-Khiem Le, Vlado Menkovski, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Nguyen, Van-Tu Ninh, Luis A. Pérez Rey, Minh-Triet Tran, Tianyang Wang:
Extended 2D Scene Image-Based 3D Scene Retrieval. 3DOR@Eurographics 2019: 41-48 - [c19]Michiel Verburg, Vlado Menkovski:
Micro-expression detection in long videos using optical flow and recurrent neural networks. FG 2019: 1-6 - [c18]Joost Visser, Alessandro Corbetta, Vlado Menkovski, Federico Toschi:
Stampnet: Unsupervised Multi-Class Object Discovery. ICIP 2019: 2951-2955 - [c17]Loek Tonnaer, Jiapeng Li, Vladimir Osin, Mike Holenderski, Vlado Menkovski:
Anomaly Detection for Visual Quality Control of 3D-Printed Products. IJCNN 2019: 1-8 - [c16]Yuhao Wang, Vlado Menkovski, Ivan Wang Hei Ho, Mykola Pechenizkiy:
VANET Meets Deep Learning: The Effect of Packet Loss on the Object Detection Performance. VTC Spring 2019: 1-5 - [p1]Stefan Thaler, Vlado Menkovski:
The Role of Deep Learning in Improving Healthcare. Data Science for Healthcare 2019: 75-116 - [i12]Luis A. Pérez Rey, Vlado Menkovski, Jacobus W. Portegies:
Diffusion Variational Autoencoders. CoRR abs/1901.08991 (2019) - [i11]Joost Visser, Alessandro Corbetta, Vlado Menkovski, Federico Toschi:
StampNet: unsupervised multi-class object discovery. CoRR abs/1902.02693 (2019) - [i10]Marijn van Knippenberg, Vlado Menkovski, Sergio Consoli:
Evolutionary Construction of Convolutional Neural Networks. CoRR abs/1903.01895 (2019) - [i9]Michiel Verburg, Vlado Menkovski:
Micro-expression detection in long videos using optical flow and recurrent neural networks. CoRR abs/1903.10765 (2019) - [i8]Niels Hellinga, Vlado Menkovski:
Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning. CoRR abs/1905.09523 (2019) - [i7]Iftitahu Ni'mah, Vlado Menkovski, Mykola Pechenizkiy:
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation. CoRR abs/1909.09485 (2019) - [i6]Alessandro Corbetta, Vlado Menkovski, Roberto Benzi, Federico Toschi:
Deep learning velocity signals allows to quantify turbulence intensity. CoRR abs/1911.05718 (2019) - 2018
- [c15]Devinder Kumar, Vlado Menkovski, Graham W. Taylor, Alexander Wong:
Understanding anatomy classification through attentive response maps. ISBI 2018: 1130-1133 - [c14]Stefan Thaler, Vlado Menkovski, Milan Petkovic:
Deep Metric Learning for Sequential Data Using Approximate Information. MLDM (1) 2018: 269-282 - [c13]Marijn van Knippenberg, Vlado Menkovski, Sergio Consoli:
Evolutionary Construction of Convolutional Neural Networks. LOD 2018: 293-304 - [c12]Evertjan Peer, Vlado Menkovski, Yingqian Zhang, Wan-Jui Lee:
Shunting Trains with Deep Reinforcement Learning. SMC 2018: 3063-3068 - [i5]Stefan Thaler, Vlado Menkovski, Milan Petkovic:
Deep Learning in Information Security. CoRR abs/1809.04332 (2018) - [i4]Nazly Rocio Santos Buitrago, Loek Tonnaer, Vlado Menkovski, Dimitrios Mavroeidis:
Anomaly Detection for imbalanced datasets with Deep Generative Models. CoRR abs/1811.00986 (2018) - 2017
- [c11]Alessandro Corbetta, Vlado Menkovski, Federico Toschi:
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields. AVSS 2017: 1-6 - [c10]Stefan Thaler, Vlado Menkovski, Milan Petkovic:
Unsupervised Signature Extraction from Forensic Logs. ECML/PKDD (3) 2017: 305-316 - [i3]Alessandro Corbetta, Vlado Menkovski, Federico Toschi:
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields. CoRR abs/1706.02850 (2017) - 2016
- [i2]Devinder Kumar, Vlado Menkovski:
Understanding Anatomy Classification Through Visualization. CoRR abs/1611.06284 (2016) - 2015
- [b1]Vlado Menkovski:
Computational Inference and Control of Quality in Multimedia Services. Springer 2015, ISBN 978-3-319-24790-8, pp. 1-135 - [j5]George Exarchakos, Luca Druda, Vlado Menkovski, Antonio Liotta:
Network analysis on Skype end-to-end video quality. Int. J. Pervasive Comput. Commun. 11(1): 17-42 (2015) - [i1]Vlado Menkovski, Zharko Aleksovski, Axel Saalbach, Hannes Nickisch:
Can Pretrained Neural Networks Detect Anatomy? CoRR abs/1512.05986 (2015) - 2013
- [j4]George Exarchakos, Luca Druda, Vlado Menkovski, Paolo Bellavista, Antonio Liotta:
Skype Resilience to High Motion Videos. Int. J. Wavelets Multiresolution Inf. Process. 11(3) (2013) - [c9]Vlado Menkovski, Antonio Liotta:
Intelligent control for adaptive video streaming. ICCE 2013: 127-128 - [c8]Antonio Liotta, Decebal Constantin Mocanu, Vlado Menkovski, Luciana Cagnetta, Georgios Exarchakos:
Instantaneous Video Quality Assessment for lightweight devices. MoMM 2013: 525 - 2012
- [j3]Vlado Menkovski, Antonio Liotta:
Adaptive psychometric scaling for video quality assessment. Signal Process. Image Commun. 27(8): 788-799 (2012) - [c7]Jewel Okyere-Benya, Mamoon Aldiabat, Vlado Menkovski, George Exarchakos, Antonio Liotta:
Video quality degradation on IPTV networks. ICNC 2012: 702-707 - [c6]Antonio Liotta, Luca Druda, Vlado Menkovski, Georgios Exarchakos:
Quality of experience management for video streams: the case of Skype. MoMM 2012: 84-92 - 2011
- [j2]Vlado Menkovski, Georgios Exarchakos, Antonio Liotta:
The Value of Relative Quality in Video Delivery. J. Mobile Multimedia 7(3): 151-162 (2011) - [c5]Vlado Menkovski, Georgios Exarchakos, Antonio Liotta:
Tackling the Sheer Scale of Subjective QoE. MobiMedia 2011: 1-15 - [c4]Georgios Exarchakos, Vlado Menkovski, Antonio Liotta:
Can Skype be used beyond video calling? MoMM 2011: 155-161 - 2010
- [j1]Vlado Menkovski, Georgios Exarchakos, Antonio Liotta, Antonio Cuadra Sánchez:
Quality of Experience Models for Multimedia Streaming. Int. J. Mob. Comput. Multim. Commun. 2(4): 1-20 (2010) - [c3]Vlado Menkovski, Georgios Exarchakos, Antonio Liotta:
Machine Learning Approach for Quality of Experience Aware Networks. INCoS 2010: 461-466
2000 – 2009
- 2009
- [c2]Vlado Menkovski, Adetola Oredope, Antonio Liotta, Antonio Cuadra Sánchez:
Predicting quality of experience in multimedia streaming. MoMM 2009: 52-59 - 2008
- [c1]Vlado Menkovski, Dimitrios Metafas:
AI Model for Computer games based on Case Based Reasoning and AI Planning. DIMEA 2008: 295-302
Coauthor Index
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