no code implementations • 19 Mar 2024 • Marko Petković, José Manuel Vicent-Luna, Vlado Menkovski, Sofía Calero
The predictions obtained from the Machine Learning model are in agreement with the values obtained from the Monte Carlo simulations, confirming that the model can be used for property prediction.
1 code implementation • 29 Nov 2023 • 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.
1 code implementation • 20 Nov 2023 • Wouter W. L. Nuijten, Vlado Menkovski
Our aim is to model a distribution over the node label assignments in settings where the tree data structure is associated with node attributes (typically high dimensional embeddings).
no code implementations • 30 Oct 2023 • Iftitahu Ni'mah, Samaneh Khoshrou, Vlado Menkovski, Mykola Pechenizkiy
Interestingly, although in general the absolute advantage of learning embeddings through label supervision is highly positive across evaluation datasets, KeyGen2Vec is shown to be competitive with classifier that exploits topic label supervision in Yahoo!
no code implementations • 30 May 2023 • Yoeri Poels, Gijs Derks, Egbert Westerhof, Koen Minartz, Sven Wiesen, Vlado Menkovski
State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data.
7 code implementations • 15 May 2023 • Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy
Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations of NLG systems via pairwise evaluation.
1 code implementation • 4 Apr 2023 • Marko Petković, Pablo Romero-Marimon, Vlado Menkovski, Sofia Calero
In this paper, we develop a model which incorporates the symmetries of the unit cell of a crystal in its architecture and explicitly models the porous structure.
1 code implementation • 28 Nov 2022 • Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu
Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully trained dense networks at initialization, without any optimization of the weights of the network (i. e., untrained networks).
no code implementations • 17 Nov 2022 • Simon M. Koop, Mark A. Peletier, Jacobus W. Portegies, Vlado Menkovski
Neural Stochastic Differential Equations (NSDE) have been trained as both Variational Autoencoders, and as GANs.
1 code implementation • 26 Oct 2022 • Hugo Melchers, Daan Crommelin, Barry Koren, Vlado Menkovski, Benjamin Sanderse
Of the two trajectory fitting procedures, the discretise-then-optimise approach produces more accurate models than the optimise-then-discretise approach.
no code implementations • 2 Oct 2022 • 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.
1 code implementation • 23 Aug 2022 • Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy
We call our method Lottery Pools.
no code implementations • 30 May 2022 • Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu
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 sparse neural networks from scratch.
no code implementations • 16 Dec 2021 • Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy
In this work, we advance the few-shot learning towards this more challenging scenario, the semantic-based few-shot learning, and propose a method to address the paradigm by capturing the inner semantic relationships using interactive psychometric learning.
1 code implementation • 1 Oct 2021 • Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy
In this paper, we present the Calibrated Adversarial Training, a method that reduces the adverse effects of semantic perturbations in adversarial training.
1 code implementation • 13 Sep 2021 • Dominique Sommers, Vlado Menkovski, Dirk Fahland
In this paper, we explore the problem of supervised learning of a process discovery technique D. We introduce a technique for training an ML-based model D using graph convolutional neural networks; D translates a given input event log into a sound Petri net.
1 code implementation • Findings (EMNLP) 2021 • Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications.
1 code implementation • 20 Aug 2021 • Yoeri Poels, Vlado Menkovski
An explanation is specified as a set of transformations of the input datapoint, with each step depicting a concept changing towards the contrastive class.
no code implementations • 7 Jul 2021 • Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy
One of the major challenges in the supervised learning approaches is expressing and collecting the rich knowledge that experts have with respect to the meaning present in the image data.
1 code implementation • 6 Jul 2021 • Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy
Adversarial training is an approach for increasing model's resilience against adversarial perturbations.
1 code implementation • 19 Apr 2021 • Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy
Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptible changes to the inputs.
1 code implementation • 16 Apr 2021 • Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy
Although various approaches have been proposed to solve this problem, two major limitations exist: (1) unsupervised approaches usually work much less efficiently due to the lack of supervisory signal, and (2) existing anomaly detection methods only use local contextual information to detect anomalous nodes, e. g., one- or two-hop information, but ignore the global contextual information.
Self-Supervised Anomaly Detection Supervised Anomaly Detection
no code implementations • 26 Nov 2020 • Luis A. Pérez Rey, Loek Tonnaer, Vlado Menkovski, Mike Holenderski, Jacobus W. Portegies
We propose a metric for the evaluation of the level of LSBD that a data representation achieves.
1 code implementation • NeurIPS 2021 • Loek Tonnaer, Luis A. Pérez Rey, Vlado Menkovski, Mike Holenderski, Jacobus W. Portegies
The definition of Linear Symmetry-Based Disentanglement (LSBD) formalizes the notion of linearly disentangled representations, but there is currently no metric to quantify LSBD.
1 code implementation • 7 Nov 2020 • Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy
In addition, it achieves comparable performance of adversarial robustness on MNIST dataset under white-box attack, and it achieves better performance than adv. PGD under white-box attack and effectively defends the transferable adversarial attack on CIFAR-10 dataset.
no code implementations • 28 Sep 2020 • Loek Tonnaer, Luis Armando Pérez Rey, Vlado Menkovski, Mike Holenderski, Jacobus W. Portegies
Although several works focus on learning LSBD representations, such methods require supervision on the underlying transformations for the entire dataset, and cannot deal with unlabeled data.
no code implementations • 22 Sep 2020 • Marijn van Knippenberg, Mike Holenderski, Vlado Menkovski
Deep Learning may provide solutions which are less time-consuming and of higher quality at large scales, as it generally does not need to generate solutions in an iterative manner, and Deep Learning models have shown a surprising capacity for solving complex tasks in recent years.
no code implementations • 14 Jun 2020 • Georgios Vlassopoulos, Tim van Erven, Henry Brighton, Vlado Menkovski
We address this by introducing a new benchmark data set with artificially generated Iris images, and showing that we can recover the latent attributes that locally determine the class.
no code implementations • 14 Apr 2020 • Lu Yin, Vlado Menkovski, Mykola Pechenizkiy
The main reason for such a reductionist approach is the difficulty in eliciting the domain knowledge from the experts.
no code implementations • 15 Jan 2020 • Yuhao Wang, Vlado Menkovski, Hao Wang, Xin Du, Mykola Pechenizkiy
As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs.
no code implementations • 14 Jan 2020 • Joris Willems, Alessandro Corbetta, Vlado Menkovski, Federico Toschi
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 in shapes, postures and walking gestures.
no code implementations • 13 Nov 2019 • Alessandro Corbetta, Vlado Menkovski, Roberto Benzi, Federico Toschi
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 length- and time-scales, and it can be quantitatively described only in terms of statistical averages.
no code implementations • 17 Sep 2019 • Iftitahu Ni'mah, Vlado Menkovski, Mykola Pechenizkiy
This study mainly investigates two decoding problems in neural keyphrase generation: sequence length bias and beam diversity.
no code implementations • 23 May 2019 • Niels Hellinga, Vlado Menkovski
Many tasks such as retrieval and recommendations can significantly benefit from structuring the data, commonly in a hierarchical way.
no code implementations • 26 Mar 2019 • Michiel Verburg, Vlado Menkovski
This paper presents a novel micro-expression spotting method using a recurrent neural network (RNN) on optical flow features.
1 code implementation • 7 Feb 2019 • Joost Visser, Alessandro Corbetta, Vlado Menkovski, Federico Toschi
Unsupervised object discovery in images involves uncovering recurring patterns that define objects and discriminates them against the background.
2 code implementations • 25 Jan 2019 • Luis A. Pérez Rey, Vlado Menkovski, Jacobus W. Portegies
A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets.
no code implementations • 2 Jan 2019 • Marijn van Knippenberg, Vlado Menkovski, Sergio Consoli
It combines deep neural networks and evolutionary algorithms to improve and/or automate the construction of neural networks.
no code implementations • 2 Nov 2018 • Nazly Rocio Santos Buitrago, Loek Tonnaer, Vlado Menkovski, Dimitrios Mavroeidis
We train a generative model without supervision on the `negative' (common) datapoints and use this model to estimate the likelihood of unseen data.
no code implementations • 12 Sep 2018 • Stefan Thaler, Vlado Menkovski, Milan Petkovic
Machine learning has a long tradition of helping to solve complex information security problems that are difficult to solve manually.
no code implementations • 9 Jun 2017 • Alessandro Corbetta, Vlado Menkovski, Federico Toschi
Even though hand-crafted image analysis algorithms are successful in many common cases, they fail frequently when there are complex interactions of multiple objects in the image.
no code implementations • 19 Nov 2016 • Devinder Kumar, Vlado Menkovski, Graham W. Taylor, Alexander Wong
One of the main challenges for broad adoption of deep learning based models such as convolutional neural networks (CNN), is the lack of understanding of their decisions.
no code implementations • 18 Dec 2015 • Vlado Menkovski, Zharko Aleksovski, Axel Saalbach, Hannes Nickisch
Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant.