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Found 16 papers, 7 papers with code
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
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.
Heavy-tailed denoising score matching
Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM).
Attentional meta-learners for few-shot polythetic classification
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.
On Second Order Behaviour in Augmented Neural ODEs: A Short Summary
In Norcliffe et al.[13], we discussed and systematically analysed how Neural ODEs (NODEs) can learn higher-order order dynamics.
Discovering outliers in the Mars Express thermal power consumption patterns
The Mars Express (MEX) spacecraft has been orbiting Mars since 2004.
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data.
Attentional meta-learners are polythetic classifiers
In contrast, attentional classifiers are polythetic by default and able to solve these problems with a linear embedding dimension.
$\alpha$-VAEs : Optimising variational inference by learning data-dependent divergence skew
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 $\alpha$.
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
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
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.
Incorporating network based protein complex discovery into automated model construction
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
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.
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
We examine the problem of controlling divergences for latent space regularisation in variational autoencoders.
On Second Order Behaviour in Augmented Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.
Ranked #22 on
Image Classification
on MNIST
Equation Discovery for Nonlinear System Identification
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.
Machine learning for predicting thermal power consumption of the Mars Express Spacecraft
The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range.
