Search Results for author: Nikola Simidjievski

Found 25 papers, 12 papers with code

Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts

1 code implementation25 Nov 2023 Jonas Jürß, Lucie Charlotte Magister, Pietro Barbiero, Pietro Liò, Nikola Simidjievski

A line of interpretable methods approach this by discovering a small set of relevant concepts as subgraphs in the last GNN layer that together explain the prediction.

Drug Discovery Travel Time Estimation

HEALNet -- Hybrid Multi-Modal Fusion for Heterogeneous Biomedical Data

no code implementations15 Nov 2023 Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik

Technological advances in medical data collection such as high-resolution histopathology and high-throughput genomic sequencing have contributed to the rising requirement for multi-modal biomedical modelling, specifically for image, tabular, and graph data.

Survival Analysis whole slide images

SHARCS: Shared Concept Space for Explainable Multimodal Learning

1 code implementation1 Jul 2023 Gabriele Dominici, Pietro Barbiero, Lucie Charlotte Magister, Pietro Liò, Nikola Simidjievski

Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given modelling task.

Retrieval

Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs

no code implementations27 Jun 2023 Navindu Leelarathna, Andrei Margeloiu, Mateja Jamnik, Nikola Simidjievski

Variational Autoencoders and their many variants have displayed impressive ability to perform dimensionality reduction, often achieving state-of-the-art performance.

Data Augmentation Dimensionality Reduction +1

ProtoGate: Prototype-based Neural Networks with Local Feature Selection for Tabular Biomedical Data

no code implementations21 Jun 2023 Xiangjian Jiang, Andrei Margeloiu, Nikola Simidjievski, Mateja Jamnik

In this paper, we propose ProtoGate, a prototype-based neural model that introduces an inductive bias by attending to both homogeneity and heterogeneity across samples.

feature selection Inductive Bias

GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data

no code implementations11 Nov 2022 Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

We create a graph between samples for each data dimension, and utilise Graph Neural Networks (GNNs) for extracting this implicit structure, and for conditioning the parameters of the first layer of an underlying predictor network.

Vocal Bursts Intensity Prediction

Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021

no code implementations5 Aug 2022 Dragi Kocev, Nikola Simidjievski, Ana Kostovska, Ivica Dimitrovski, Žiga Kokalj

The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021).

BIG-bench Machine Learning Image Segmentation +1

Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification

2 code implementations14 Jul 2022 Ivica Dimitrovski, Ivan Kitanovski, Dragi Kocev, Nikola Simidjievski

We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO).

Classification Earth Observation +4

AiTLAS: Artificial Intelligence Toolbox for Earth Observation

1 code implementation21 Jan 2022 Ivica Dimitrovski, Ivan Kitanovski, Panče Panov, Nikola Simidjievski, Dragi Kocev

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets.

Benchmarking Earth Observation +2

Heavy-tailed denoising score matching

1 code implementation17 Dec 2021 Jacob Deasy, Nikola Simidjievski, Pietro Liò

Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM).

Denoising

On Second Order Behaviour in Augmented Neural ODEs: A Short Summary

no code implementations NeurIPS Workshop DLDE 2021 Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lio

In Norcliffe et al.[13], we discussed and systematically analysed how Neural ODEs (NODEs) can learn higher-order order dynamics.

Attentional Meta-learners for Few-shot Polythetic Classification

1 code implementation9 Jun 2021 Ben Day, Ramon Viñas, Nikola Simidjievski, Pietro Liò

Polythetic classifications, based on shared patterns of features that need neither be universal nor constant among members of a class, are common in the natural world and greatly outnumber monothetic classifications over a set of features.

Classification feature selection +1

$\alpha$-VAEs : Optimising variational inference by learning data-dependent divergence skew

no code implementations ICML Workshop INNF 2021 Jacob Deasy, Tom Andrew McIver, Nikola Simidjievski, Pietro Lio

The {\em skew-geometric Jensen-Shannon divergence} $\left(\textrm{JS}^{\textrm{G}_{\alpha}}\right)$ allows for an intuitive interpolation between forward and reverse Kullback-Leibler (KL) divergence based on the skew parameter $\alpha$.

Denoising Variational Inference

Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training

1 code implementation2 Dec 2020 Andrei Margeloiu, Nikola Simidjievski, Mateja Jamnik, Adrian Weller

We investigate the influence of adversarial training on the interpretability of convolutional neural networks (CNNs), specifically applied to diagnosing skin cancer.

Incorporating network based protein complex discovery into automated model construction

no code implementations29 Sep 2020 Paul Scherer, Maja Trȩbacz, Nikola Simidjievski, Zohreh Shams, Helena Andres Terre, Pietro Liò, Mateja Jamnik

We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs.

Clustering

Fuzzy Jaccard Index: A robust comparison of ordered lists

2 code implementations5 Aug 2020 Matej Petković, Blaž Škrlj, Dragi Kocev, Nikola Simidjievski

In real-life, and in particular high-dimensional domains, where only a small percentage of the whole feature space might be relevant, a robust and confident feature ranking leads to interpretable findings as well as efficient computation and good predictive performance.

BIG-bench Machine Learning

On Second Order Behaviour in Augmented Neural ODEs

1 code implementation NeurIPS 2020 Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Liò

Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.

Image Classification

Equation Discovery for Nonlinear System Identification

no code implementations1 Jul 2019 Nikola Simidjievski, Ljupčo Todorovski, Juš Kocijan, Sašo Džeroski

In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated on two continuous-time case studies.

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