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Found 32 papers, 9 papers with code
Knowledge graphs for empirical concept retrieval
Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz.\ as a tool for personalized explainability.
Hubness Reduction Improves Sentence-BERT Semantic Spaces
We find that when hubness is high, we can reduce error rate and hubness using hubness reduction methods.
Concept-based explainability for an EEG transformer model
Deep learning models are complex due to their size, structure, and inherent randomness in training procedures.
Using Sequences of Life-events to Predict Human Lives
We can also represent human lives in a way that shares this structural similarity to language.
Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-global interactions in every decoder transformer block through attention heads of several distinct local and global windows.
On convex decision regions in deep network representations
G{\"a}rdenfors' conceptual spaces is a prominent framework for understanding human representations.
Robustness of Visual Explanations to Common Data Augmentation
As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever.
On the role of Model Uncertainties in Bayesian Optimization
In this work, we provide an extensive study of the relationship between the BO performance (regret) and uncertainty calibration for popular surrogate models and compare them across both synthetic and real-world experiments.
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts
We then observe how the cosine similarities decrease as transcription noise increases and conclude that even when automatic speech recognition transcripts are erroneous, it is still possible to obtain high-quality topic embeddings from the transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Generalization by design: Shortcuts to Generalization in Deep Learning
Instead, we show that good generalization may be instigated by bounded spectral products over layers leading to a novel geometric regularizer.
A simple defense against adversarial attacks on heatmap explanations
With machine learning models being used for more sensitive applications, we rely on interpretability methods to prove that no discriminating attributes were used for classification.
Probabilistic Decoupling of Labels in Classification
In this paper we develop a principled, probabilistic, unified approach to non-standard classification tasks, such as semi-supervised, positive-unlabelled, multi-positive-unlabelled and noisy-label learning.
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline
In this paper, we revisit the popularity prediction on Instagram.
IROF: a low resource evaluation metric for explanation methods
The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods.
Robustness analytics to data heterogeneity in edge computing
Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge.
Aggregating explanation methods for neural networks stabilizes explanations
Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.
Active Learning Solution on Distributed Edge Computing
All the requirements to realize the convergence is integrated on the Fog Platform.
Probabilistic Decoupling of Labels in Classification
We investigate probabilistic decoupling of labels supplied for training, from the underlying classes for prediction.
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
It has been shown that learning signal structure in terms of principal components is dependent on the ratio of sample size and dimensionality and that a critical number of observations is needed before learning starts (Biehl and Mietzner, 1993).
Aggregating explanation methods for stable and robust explainability
Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.
Multi-View Bayesian Correlated Component Analysis
Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus.
Latent Space Oddity: on the Curvature of Deep Generative Models
Deep generative models provide a systematic way to learn nonlinear data distributions, through a set of latent variables and a nonlinear "generator" function that maps latent points into the input space.
Adaptive Smoothing in fMRI Data Processing Neural Networks
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing.
EEG source imaging assists decoding in a face recognition task
In this work we explore the generality of Edelman et al. hypothesis by considering decoding of face recognition.
Neurons and Cognition
Towards end-to-end optimisation of functional image analysis pipelines
The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pipeline of data processing modules, each involving a variety of parameters.
A Locally Adaptive Normal Distribution
The multivariate normal density is a monotonic function of the distance to the mean, and its ellipsoidal shape is due to the underlying Euclidean metric.
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
We then learn a class-specific probabilistic generative models of the transformations in a Riemannian submanifold of the Lie group of diffeomorphisms.
Bayesian inference for spatio-temporal spike-and-slab priors
In this work, we address the problem of solving a series of underdetermined linear inverse problems subject to a sparsity constraint.
Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems
We are interested in solving the multiple measurement vector (MMV) problem for instances, where the underlying sparsity pattern exhibit spatio-temporal structure motivated by the electroencephalogram (EEG) source localization problem.
A Topic Model Approach to Multi-Modal Similarity
Calculating similarities between objects defined by many heterogeneous data modalities is an important challenge in many multimedia applications.
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation
In this work, we propose to use dimensionality reduction of the user-website interaction graph in order to produce simplified features of users and websites that can be used as predictors of clickthrough rate.
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis.
