Search Results for author: Nikola Simidjievski

Found 16 papers, 7 papers with code

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.

Semantic Segmentation Type prediction

Heavy-tailed denoising score matching

no code implementations17 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

Attentional meta-learners for few-shot polythetic classification

no code implementations29 Sep 2021 Ben Day, Ramon Viñas Torné, Nikola Simidjievski, Pietro Lio

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

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 are polythetic classifiers

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

In contrast, attentional classifiers are polythetic by default and able to solve these problems with a linear embedding dimension.

feature selection Few-Shot Learning

$\alpha$-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.

Using ontology embeddings for structural inductive bias in gene expression data analysis

no code implementations22 Nov 2020 Maja Trębacz, Zohreh Shams, Mateja Jamnik, Paul Scherer, Nikola Simidjievski, Helena Andres Terre, Pietro Liò

Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.

Survival Analysis

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.

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.

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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