
Generalization by design: Shortcuts to Generalization in Deep Learning
We take a geometrical viewpoint and present a unifying view on supervise...
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What can we learn from gradients?
Recent work (<cit.>) has shown that it is possible to reconstruct the in...
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A simple defense against adversarial attacks on heatmap explanations
With machine learning models being used for more sensitive applications,...
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On the Limits to MultiModal Popularity Prediction on Instagram – A New Robust, Efficient and Explainable Baseline
The predictability of social media popularity is a topic of much scienti...
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Towards Federated Learning: Robustness Analytics to Data Heterogeneity
Federated Learning allows remote centralized server training models with...
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Active Learning Solution on Distributed Edge Computing
Industry 4.0 becomes possible through the convergence between Operationa...
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Probabilistic Decoupling of Labels in Classification
We investigate probabilistic decoupling of labels supplied for training,...
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Phase transition in PCA with missing data: Reduced signaltonoise ratio, not sample size!
How does missing data affect our ability to learn signal structures? It ...
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Aggregating explainability methods for neural networks stabilizes explanations
Despite a growing literature on explaining neural networks, no consensus...
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MultiView Bayesian Correlated Component Analysis
Correlated component analysis as proposed by Dmochowski et al. (2012) is...
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Latent Space Oddity: on the Curvature of Deep Generative Models
Deep generative models provide a systematic way to learn nonlinear data ...
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Adaptive Smoothing in fMRI Data Processing Neural Networks
Functional Magnetic Resonance Imaging (fMRI) relies on multistep data p...
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Towards endtoend optimisation of functional image analysis pipelines
The study of neurocognitive tasks requiring accurate localisation of act...
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A Locally Adaptive Normal Distribution
The multivariate normal density is a monotonic function of the distance ...
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Dreaming More Data: Classdependent Distributions over Diffeomorphisms for Learned Data Augmentation
Data augmentation is a key element in training highdimensional models. ...
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Bayesian inference for spatiotemporal spikeandslab priors
In this work, we address the problem of solving a series of underdetermi...
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Spatiotemporal Spike and Slab Priors for Multiple Measurement Vector Problems
We are interested in solving the multiple measurement vector (MMV) probl...
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A Topic Model Approach to MultiModal Similarity
Calculating similarities between objects defined by many heterogeneous d...
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Dimensionality reduction for clickthrough rate prediction: Dense versus sparse representation
In online advertising, display ads are increasingly being placed based o...
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Lars Kai Hansen
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