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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...
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06/05/2023
Using Sequences of Life-events to Predict Human Lives
Over the past decade, machine learning has revolutionized computers' abi...
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06/01/2023
Masked Autoencoders with Multi-Window Attention Are Better Audio Learners
Several recent works have adapted Masked Autoencoders (MAEs) for learnin...
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05/26/2023
On convex conceptual regions in deep network representations
The current study of human-machine alignment aims at understanding the g...
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04/18/2023
Robustness of Visual Explanations to Common Data Augmentation
As the use of deep neural networks continues to grow, understanding thei...
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01/14/2023
On the role of Model Uncertainties in Bayesian Optimization
Bayesian optimization (BO) is a popular method for black-box optimizatio...
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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 ...
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07/05/2021
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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10/29/2020
What can we learn from gradients?
Recent work (<cit.>) has shown that it is possible to reconstruct the in...
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07/13/2020
A simple defense against adversarial attacks on heatmap explanations
With machine learning models being used for more sensitive applications,...
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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...
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02/12/2020
Towards Federated Learning: Robustness Analytics to Data Heterogeneity
Federated Learning allows remote centralized server training models with...
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06/25/2019
Active Learning Solution on Distributed Edge Computing
Industry 4.0 becomes possible through the convergence between Operationa...
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05/29/2019
Probabilistic Decoupling of Labels in Classification
We investigate probabilistic decoupling of labels supplied for training,...
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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 ...
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03/01/2019
Aggregating explainability methods for neural networks stabilizes explanations
Despite a growing literature on explaining neural networks, no consensus...
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02/07/2018
Multi-View Bayesian Correlated Component Analysis
Correlated component analysis as proposed by Dmochowski et al. (2012) is...
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10/31/2017
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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10/02/2017
Adaptive Smoothing in fMRI Data Processing Neural Networks
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data p...
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10/13/2016
Towards end-to-end optimisation of functional image analysis pipelines
The study of neurocognitive tasks requiring accurate localisation of act...
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06/08/2016
A Locally Adaptive Normal Distribution
The multivariate normal density is a monotonic function of the distance ...
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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. ...
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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...
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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...
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05/27/2014
A Topic Model Approach to Multi-Modal Similarity
Calculating similarities between objects defined by many heterogeneous d...
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11/27/2013
