Lars Kai Hansen | DeepAI

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∙ 07/24/2023

Concept-based explainability for an EEG transformer model

Deep learning models are complex due to their size, structure, and inher...

0 Anders Gjølbye Madsen, et al. ∙

∙ 06/05/2023

Using Sequences of Life-events to Predict Human Lives

Over the past decade, machine learning has revolutionized computers' abi...

0 Germans Savcisens, et al. ∙

∙ 06/01/2023

Masked Autoencoders with Multi-Window Attention Are Better Audio Learners

Several recent works have adapted Masked Autoencoders (MAEs) for learnin...

0 Sarthak Yadav, et al. ∙

∙ 05/26/2023

On convex conceptual regions in deep network representations

The current study of human-machine alignment aims at understanding the g...

0 Lenka Tětková, et al. ∙

∙ 04/18/2023

Robustness of Visual Explanations to Common Data Augmentation

As the use of deep neural networks continues to grow, understanding thei...

0 Lenka Tětková, et al. ∙

∙ 01/14/2023

On the role of Model Uncertainties in Bayesian Optimization

Bayesian optimization (BO) is a popular method for black-box optimizatio...

0 Jonathan Foldager, et al. ∙

∙ 09/25/2021

Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts

For a multilingual podcast streaming service, it is critical to be able ...

0 Raluca Alexandra Fetic, et al. ∙

∙ 07/05/2021

Generalization by design: Shortcuts to Generalization in Deep Learning

We take a geometrical viewpoint and present a unifying view on supervise...

0 Petr Taborsky, et al. ∙

∙ 10/29/2020

What can we learn from gradients?

Recent work (<cit.>) has shown that it is possible to reconstruct the in...

0 Jia Qian, et al. ∙

∙ 07/13/2020

A simple defense against adversarial attacks on heatmap explanations

With machine learning models being used for more sensitive applications,...

9 Laura Rieger, et al. ∙

∙ 04/26/2020

On the Limits to Multi-Modal Popularity Prediction on Instagram – A New Robust, Efficient and Explainable Baseline

The predictability of social media popularity is a topic of much scienti...

0 Christoffer Riis, et al. ∙

∙ 02/12/2020

Towards Federated Learning: Robustness Analytics to Data Heterogeneity

Federated Learning allows remote centralized server training models with...

0 Jia Qian, et al. ∙

∙ 06/25/2019

Active Learning Solution on Distributed Edge Computing

Industry 4.0 becomes possible through the convergence between Operationa...

0 Jia Qian, et al. ∙

∙ 05/29/2019

Probabilistic Decoupling of Labels in Classification

We investigate probabilistic decoupling of labels supplied for training,...

0 Jeppe Nørregaard, et al. ∙

∙ 05/02/2019

Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!

How does missing data affect our ability to learn signal structures? It ...

0 Niels Bruun Ipsen, et al. ∙

∙ 03/01/2019

Aggregating explainability methods for neural networks stabilizes explanations

Despite a growing literature on explaining neural networks, no consensus...

0 Laura Rieger, et al. ∙

∙ 02/07/2018

Multi-View Bayesian Correlated Component Analysis

Correlated component analysis as proposed by Dmochowski et al. (2012) is...

0 Simon Kamronn, et al. ∙

∙ 10/31/2017

Latent Space Oddity: on the Curvature of Deep Generative Models

Deep generative models provide a systematic way to learn nonlinear data ...

0 Georgios Arvanitidis, et al. ∙

∙ 10/02/2017

Adaptive Smoothing in fMRI Data Processing Neural Networks

Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data p...

0 Albert Vilamala, et al. ∙

∙ 10/13/2016

Towards end-to-end optimisation of functional image analysis pipelines

The study of neurocognitive tasks requiring accurate localisation of act...

0 Albert Vilamala, et al. ∙

∙ 06/08/2016

A Locally Adaptive Normal Distribution

The multivariate normal density is a monotonic function of the distance ...

0 Georgios Arvanitidis, et al. ∙

∙ 10/09/2015

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation

Data augmentation is a key element in training high-dimensional models. ...

0 Søren Hauberg, et al. ∙

∙ 09/15/2015

Bayesian inference for spatio-temporal spike-and-slab priors

In this work, we address the problem of solving a series of underdetermi...

0 Michael Riis Andersen, et al. ∙

∙ 08/19/2015

Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems

We are interested in solving the multiple measurement vector (MMV) probl...

0 Michael Riis Andersen, et al. ∙

∙ 05/27/2014

A Topic Model Approach to Multi-Modal Similarity

Calculating similarities between objects defined by many heterogeneous d...

0 Rasmus Troelsgård, et al. ∙

∙ 11/27/2013

Dimensionality reduction for click-through rate prediction: Dense versus sparse representation

In online advertising, display ads are increasingly being placed based o...

0 Bjarne Ørum Fruergaard, et al. ∙

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