23 citations · 60 across the 36 of their papers we have counts for
9 papers · 1 filter
Description Generation using Variational Auto-Encoders for precursor microRNA
Marko Petković, Vlado Menkovski
Micro RNAs (miRNA) are a type of non-coding RNA, which are involved in gene regulation and can be associated with diseases such as cancer, cardiovascular and neurological diseases.…
Node Classification in Random Trees
Wouter W. L. Nuijten, Vlado Menkovski
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the…
KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering
Iftitahu Ni'mah, Samaneh Khoshrou, Vlado Menkovski +1
Representing documents into high dimensional embedding space while preserving the structural similarity between document sources has been an ultimate goal for many works on text re…
On learning latent dynamics of the AUG plasma state
A. Kit, A. E. Järvinen, Y. R. J. Poels +5
In this work, we demonstrate the utility of state representation learning applied to modeling the time evolution of electron density and temperature profiles at ASDEX-Upgrade (AUG)…
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Iftitahu Ni'mah, Meng Fang, Vlado Menkovski +1
In this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, s…
Dynamic Sparsity Is Channel-Level Sparsity Learner
Lu Yin, Gen Li, Meng Fang +7
Sparse training has received an upsurging interest in machine learning due to its tantalizing saving potential for the entire training process as well as inference. Dynamic sparse…
Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates
Yoeri Poels, Gijs Derks, Egbert Westerhof +3
Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool t…
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Koen Minartz, Yoeri Poels, Simon Koop +1
Neural networks are emerging as a tool for scalable data-driven simulation of high-dimensional dynamical systems, especially in settings where numerical methods are infeasible or c…
Equivariant Parameter Sharing for Porous Crystalline Materials
Marko Petković, Pablo Romero-Marimon, Vlado Menkovski +1
Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulatio…