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Found 22 papers, 10 papers with code
Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding
In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision.
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others.
Self-Supervised Learning for Few-Shot Bird Sound Classification
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost.
Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields
We show that in-situ observations with richer temporal resolution represent an added value in terms of the model reconstruction performance.
Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio.
A Strong and Simple Deep Learning Baseline for BCI MI Decoding
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
The bioacoustic community recasted the problem of sound event detection within the framework of few-shot learning, i. e. training a system with only few labeled examples.
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
BCI Motor Imagery datasets usually are small and have different electrodes setups.
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
In addition, when combining class labels with metadata using multiple supervised contrastive learning, an extension of supervised contrastive learning solving an additional task of grouping patients within the same sex and age group, more informative features are learned.
Learning-based estimation of in-situ wind speed from underwater acoustics
As sea surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-related information.
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals.
A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models
Real-world generalization, e. g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences.
Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification
It is very common to face classification problems where the number of available labeled samples is small compared to their dimension.
Few-shot Decoding of Brain Activation Maps
Few-shot learning addresses problems for which a limited number of training examples are available.
Gradients of Connectivity as Graph Fourier Bases of Brain Activity
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neuroscience.
Efficient Hardware Implementation of Incremental Learning and Inference on Chip
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip.
Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging
Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains.
Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning
Taken together, this contribution extends previous attempts on estimating encoding models, by showing the ability to model brain activity using a generic DNN (ie not specifically trained for this purpose) to extract auditory features, suggesting a degree of similarity between internal DNN representations and brain activity in naturalistic settings.
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging.
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection.
Transfer Incremental Learning using Data Augmentation
Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power.
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals.
