Vlado Menkovski

research

∙ 05/30/2023

Dynamic Sparsity Is Channel-Level Sparsity Learner

Sparse training has received an upsurging interest in machine learning d...

1 Lu Yin, et al. ∙

research

∙ 05/30/2023

Fast Dynamic 1D Simulation of Divertor Plasmas with Neural PDE Surrogates

Managing divertor plasmas is crucial for operating reactor scale tokamak...

0 Yoeri Poels, et al. ∙

research

∙ 05/23/2023

Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics

Neural networks are emerging as a tool for scalable data-driven simulati...

0 Koen Minartz, et al. ∙

research

∙ 05/15/2023

NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist

In this study, we analyze NLG automatic metrics based on whether human e...

6 Iftitahu Ni'mah, et al. ∙

research

∙ 04/04/2023

Equivariant Networks for Porous Crystalline Materials

Efficiently predicting properties of porous crystalline materials has gr...

0 Marko Petkovic, et al. ∙

research

∙ 11/28/2022

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets

Recent works have impressively demonstrated that there exists a subnetwo...

12 Tianjin Huang, et al. ∙

research

∙ 11/17/2022

Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations

Neural Stochastic Differential Equations (NSDE) have been trained as bot...

0 Simon M. Koop, et al. ∙

research

∙ 10/26/2022

Comparison of neural closure models for discretised PDEs

Neural closure models have recently been proposed as a method for effici...

0 Hugo Melchers, et al. ∙

research

∙ 10/02/2022

Towards Learned Simulators for Cell Migration

Simulators driven by deep learning are gaining popularity as a tool for ...

0 Koen Minartz, et al. ∙

research

∙ 08/23/2022

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost

Lottery tickets (LTs) is able to discover accurate and sparse subnetwork...

8 Lu Yin, et al. ∙

research

∙ 05/30/2022

Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training

Recent works on sparse neural network training (sparse training) have sh...

0 Lu Yin, et al. ∙

research

∙ 12/16/2021

Semantic-Based Few-Shot Learning by Interactive Psychometric Testing

Few-shot classification tasks aim to classify images in query sets based...

8 Lu Yin, et al. ∙

research

∙ 10/01/2021

Calibrated Adversarial Training

Adversarial training is an approach of increasing the robustness of mode...

5 Tianjin Huang, et al. ∙

research

∙ 09/13/2021

Process Discovery Using Graph Neural Networks

Automatically discovering a process model from an event log is the prime...

0 Dominique Sommers, et al. ∙

research

∙ 08/27/2021

ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection

The ability to detect Out-of-Domain (OOD) inputs has been a critical req...

21 Iftitahu Ni'mah, et al. ∙

research

∙ 08/20/2021

VAE-CE: Visual Contrastive Explanation using Disentangled VAEs

The goal of a classification model is to assign the correct labels to da...

9 Yoeri Poels, et al. ∙

research

∙ 07/07/2021

Hierarchical Semantic Segmentation using Psychometric Learning

Assigning meaning to parts of image data is the goal of semantic image s...

11 Lu Yin, et al. ∙

research

∙ 07/06/2021

On Generalization of Graph Autoencoders with Adversarial Training

Adversarial training is an approach for increasing model's resilience ag...

7 Tianjin Huang, et al. ∙

research

∙ 04/19/2021

Direction-Aggregated Attack for Transferable Adversarial Examples

Deep neural networks are vulnerable to adversarial examples that are cra...

8 Tianjin Huang, et al. ∙

research

∙ 04/16/2021

Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks

Recent years have witnessed an upsurge of interest in the problem of ano...

13 Tianjin Huang, et al. ∙

research

∙ 11/26/2020

A Metric for Linear Symmetry-Based Disentanglement

The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed ...

0 Luis A. Pérez Rey, et al. ∙

research

∙ 11/11/2020

Quantifying and Learning Disentangled Representations with Limited Supervision

Learning low-dimensional representations that disentangle the underlying...

0 Loek Tonnaer, et al. ∙

research

∙ 11/07/2020

Bridging the Performance Gap between FGSM and PGD Adversarial Training

Deep learning achieves state-of-the-art performance in many tasks but ex...

9 Tianjin Huang, et al. ∙

research

∙ 09/22/2020

Complex Vehicle Routing with Memory Augmented Neural Networks

Complex real-life routing challenges can be modeled as variations of wel...

0 Marijn van Knippenberg, et al. ∙

research

∙ 06/14/2020

Explaining Predictions by Approximating the Local Decision Boundary

Constructing accurate model-agnostic explanations for opaque machine lea...

0 Georgios Vlassopoulos, et al. ∙

research

∙ 04/14/2020

Knowledge Elicitation using Deep Metric Learning and Psychometric Testing

Knowledge present in a domain is well expressed as relationships between...

0 Lu Yin, et al. ∙

research

∙ 01/15/2020

Causal Discovery from Incomplete Data: A Deep Learning Approach

As systems are getting more autonomous with the development of artificia...

127 Yuhao Wang, et al. ∙

research

∙ 01/14/2020

Pedestrian orientation dynamics from high-fidelity measurements

We investigate in real-life conditions and with very high accuracy the d...

0 Joris Willems, et al. ∙

research

∙ 11/13/2019

Deep learning velocity signals allows to quantify turbulence intensity

Turbulence, the ubiquitous and chaotic state of fluid motions, is charac...

0 Alessandro Corbetta, et al. ∙

research

∙ 09/17/2019

BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation

This study mainly investigates two decoding problems in neural keyphrase...

0 Iftitahu Ni'mah, et al. ∙

research

∙ 05/23/2019

Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning

Many tasks such as retrieval and recommendations can significantly benef...

0 Niels Hellinga, et al. ∙

research

∙ 03/26/2019

Micro-expression detection in long videos using optical flow and recurrent neural networks

Facial micro-expressions are subtle and involuntary expressions that can...

0 Michiel Verburg, et al. ∙

research

∙ 02/07/2019

StampNet: unsupervised multi-class object discovery

Unsupervised object discovery in images involves uncovering recurring pa...

0 Joost Visser, et al. ∙

research

∙ 01/25/2019

Diffusion Variational Autoencoders

A standard Variational Autoencoder, with a Euclidean latent space, is st...

0 Luis A. Pérez Rey, et al. ∙

research

∙ 01/02/2019

Evolutionary Construction of Convolutional Neural Networks

Neuro-Evolution is a field of study that has recently gained significant...

0 Marijn van Knippenberg, et al. ∙

research

∙ 11/02/2018

Anomaly Detection for imbalanced datasets with Deep Generative Models

Many important data analysis applications present with severely imbalanc...

0 Nazly Rocio Santos Buitrago, et al. ∙

research

∙ 09/12/2018

Deep Learning in Information Security

Machine learning has a long tradition of helping to solve complex inform...

0 Stefan Thaler, et al. ∙

research

∙ 06/09/2017

Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields

Overhead depth map measurements capture sufficient amount of information...

0 Alessandro Corbetta, et al. ∙

research

∙ 11/19/2016

Understanding Anatomy Classification Through Visualization

One of the main challenges for broad adoption of deep convolutional neur...

0 Devinder Kumar, et al. ∙

research

∙ 12/18/2015

Can Pretrained Neural Networks Detect Anatomy?

Convolutional neural networks demonstrated outstanding empirical results...

0 Vlado Menkovski, et al. ∙

Vlado Menkovski

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