Menkovski, Vlado

19 publications

ICML 2025 Deep Neural Cellular Potts Models Koen Minartz, Tim D’Hondt, Leon Hillmann, Jörn Starruß, Lutz Brusch, Vlado Menkovski

ICMLW 2024 Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates Yoeri Poels, Koen Minartz, Harshit Bansal, Vlado Menkovski

NeurIPSW 2024 Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale Pol Timmer, Koen Minartz, Vlado Menkovski

NeurIPS 2023 Dynamic Sparsity Is Channel-Level Sparsity Learner Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang "Atlas" Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu

ECML-PKDD 2023 Enhancing Adversarial Training via Reweighting Optimization Trajectory Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy

NeurIPS 2023 Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics Koen Minartz, Yoeri Poels, Simon Koop, Vlado Menkovski

AAAI 2023 Lottery Pools: Winning More by Interpolating Tickets Without Increasing Training or Inference Cost Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy

ECML-PKDD 2022 Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy

ICML 2022 Quantifying and Learning Linear Symmetry-Based Disentanglement Loek Tonnaer, Luis Armando Perez Rey, Vlado Menkovski, Mike Holenderski, Jim Portegies

UAI 2022 Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy

NeurIPSW 2022 Towards Learned Simulators for Cell Migration Koen Minartz, Yoeri Poels, Vlado Menkovski

LoG 2022 You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu

ACML 2021 Calibrated Adversarial Training Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

ACML 2021 Hierarchical Semantic Segmentation Using Psychometric Learning Lu Yin, Vlado Menkovski, Shwei Liu, Mykola Pechenizkiy

ECML-PKDD 2021 On Generalization of Graph Autoencoders with Adversarial Training Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy

ACML 2021 Time-Constrained Multi-Agent Path Finding in Non-Lattice Graphs with Deep Reinforcement Learning Marijn Knippenberg, Mike Holenderski, Vlado Menkovski

IJCAI 2020 Diffusion Variational Autoencoders Luis A. Pérez Rey, Vlado Menkovski, Jim Portegies

ECML-PKDD 2020 Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing Lu Yin, Vlado Menkovski, Mykola Pechenizkiy

ECML-PKDD 2017 Unsupervised Signature Extraction from Forensic Logs Stefan Thaler, Vlado Menkovski, Milan Petkovic