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Found 88 papers, 49 papers with code
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
Debiased Sinkhorn barycenters
However, entropy brings some inherent smoothing bias, resulting for example in blurred barycenters.
Deep learning-based electroencephalography analysis: a systematic review
To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Here, we propose an extrapolation technique starting from a sequence of iterates in the dual that leads to the construction of improved dual points.
Dual Extrapolation for Sparse Generalized Linear Models
Generalized Linear Models (GLM) form a wide class of regression and classification models, where prediction is a function of a linear combination of the input variables.
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control.
Faster ICA under orthogonal constraint
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences.
Faster independent component analysis by preconditioning with Hessian approximations
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences.
Spatio-Temporal Alignments: Optimal transport through space and time
In this paper, we propose Spatio-Temporal Alignments (STA), a new differentiable formulation of DTW, in which spatial differences between time samples are accounted for using regularized optimal transport (OT).
Uncovering the structure of clinical EEG signals with self-supervised learning
Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.
A deep learning architecture to detect events in EEG signals during sleep
Annotations of such events require a trained sleep expert, a time consuming and tedious process with a large inter-scorer variability.
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD.
Ranked #1 on
Sleep Arousal Detection
on MESA
Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments
These complex datasets, describing dynamics with both time and spatial components, pose new challenges for data analysis.
Implicit differentiation of Lasso-type models for hyperparameter optimization
Our approach scales to high-dimensional data by leveraging the sparsity of the solutions.
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques.
Data augmentation for learning predictive models on EEG: a systematic comparison
Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging data analysis.
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.
Beyond Pham's algorithm for joint diagonalization
The approximate joint diagonalization of a set of matrices consists in finding a basis in which these matrices are as diagonal as possible.
Robust learning from corrupted EEG with dynamic spatial filtering
Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels.
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting hand-crafted features, that exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG, EMG and EOG), and that can exploit the temporal context of each 30s window of data.
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics.
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
To deal with this, we adapt the desparsified Lasso estimator -- an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions -- to temporal data corrupted with autocorrelated noise.
Learning step sizes for unfolded sparse coding
We demonstrate that for a large class of unfolded algorithms, if the algorithm converges to the solution of the Lasso, its last layers correspond to ISTA with learned step sizes.
Self-supervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains.
Deep invariant networks with differentiable augmentation layers
Practitioners can typically enforce a desired invariance on the trained model through the choice of a network architecture, e. g. using convolutions for translations, or using data augmentation.
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals
This algorithm can be used to distribute the computation on a number of workers which scales linearly with the encoded signal's size.
Wasserstein regularization for sparse multi-task regression
We argue in this paper that these techniques fail to leverage the spatial information associated to regressors.
GAP Safe Screening Rules for Sparse-Group-Lasso
We adapt to the case of Sparse-Group Lasso recent safe screening rules that discard early in the solver irrelevant features/groups.
GAP Safe Screening Rules for Sparse-Group Lasso
For statistical learning in high dimension, sparse regularizations have proven useful to boost both computational and statistical efficiency.
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing.
HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method.
Shared Independent Component Analysis for Multi-Subject Neuroimaging
While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
Sparsity promoting norms are frequently used in high dimensional regression.
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters.
API design for machine learning software: experiences from the scikit-learn project
Scikit-learn is an increasingly popular machine learning li- brary.
Stochastic algorithms with descent guarantees for ICA
We derive an online algorithm for the streaming setting, and an incremental algorithm for the finite sum setting, with the following benefits.
Manifold-regression to predict from MEG/EEG brain signals without source modeling
We show that Wasserstein and geometric distances allow perfect out-of-sample prediction on the generative models.
Gap Safe screening rules for sparsity enforcing penalties
In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term.
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Building upon the well-known classifier two-sample test (C2ST), we introduce L-C2ST, a new method that allows for a local evaluation of the posterior estimator at any given observation.
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think
Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning.
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Nevertheless, we soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.
Convolutional Monge Mapping Normalization for learning on sleep data
In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices.
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance.
From safe screening rules to working sets for faster Lasso-type solvers
For the Lasso estimator a WS is a set of features, while for a Group Lasso it refers to a set of groups.
Diffusion posterior sampling for simulation-based inference in tall data settings
Determining which parameters of a non-linear model could best describe a set of experimental data is a fundamental problem in science and it has gained much traction lately with the rise of complex large-scale simulators (a. k. a.
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Results on multimodal neuroimaging problems with M/EEG data are also reported.
On the Consistency of Ordinal Regression Methods
We will see that, for a family of surrogate loss functions that subsumes support vector ordinal regression and ORBoosting, consistency can be fully characterized by the derivative of a real-valued function at zero, as happens for convex margin-based surrogates in binary classification.
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research.
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy
Monoclonal antibodies constitute one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors.
The iterative reweighted Mixed-Norm Estimate for spatio-temporal MEG/EEG source reconstruction
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution.
Anomaly Detection and Localisation using Mixed Graphical Models
We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model.
Mind the duality gap: safer rules for the Lasso
In this paper, we propose new versions of the so-called $\textit{safe rules}$ for the Lasso.
GAP Safe screening rules for sparse multi-task and multi-class models
The GAP Safe rule can cope with any iterative solver and we illustrate its performance on coordinate descent for multi-task Lasso, binary and multinomial logistic regression, demonstrating significant speed ups on all tested datasets with respect to previous safe rules.
Calibration of One-Class SVM for MV set estimation
Our approach makes it possible to tune the hyperparameters automatically and obtain nested set estimates.
Fast Optimal Transport Averaging of Neuroimaging Data
Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest.
Data-driven HRF estimation for encoding and decoding models
We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels.
Second order scattering descriptors predict fMRI activity due to visual textures
Second layer scattering descriptors are known to provide good classification performance on natural quasi-stationary processes such as visual textures due to their sensitivity to higher order moments and continuity with respect to small deformations.
Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals
This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG).
HRF estimation improves sensitivity of fMRI encoding and decoding models
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal.
Accelerating likelihood optimization for ICA on real signals
We study optimization methods for solving the maximum likelihood formulation of independent component analysis (ICA).
Brain covariance selection: better individual functional connectivity models using population prior
We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population.
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Inferring the location of the current sources that generated these magnetic fields is an ill-posed inverse problem known as source imaging.
Multi-subject MEG/EEG source imaging with sparse multi-task regression
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity.
Support recovery and sup-norm convergence rates for sparse pivotal estimation
In high dimensional sparse regression, pivotal estimators are estimators for which the optimal regularization parameter is independent of the noise level.
mvlearn: Multiview Machine Learning in Python
As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years.
Model identification and local linear convergence of coordinate descent
For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e. g. support identification for the Lasso) after a finite number of iterations, provided the objective function is regular enough.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
To deal with this, we adapt the desparsified Lasso estimator ---an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions--- to temporal data corrupted with autocorrelated noise.
Spectral independent component analysis with noise modeling for M/EEG source separation
Signals are modelled as a linear mixing of independent sources corrupted by additive noise, where sources and the noise are stationary Gaussian time series.
Learning summary features of time series for likelihood free inference
There has been an increasing interest from the scientific community in using likelihood-free inference (LFI) to determine which parameters of a given simulator model could best describe a set of experimental data.
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference
By contrast, we propose Adaptive multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources.
Disentangling Syntax and Semantics in the Brain with Deep Networks
The activations of language transformers like GPT-2 have been shown to linearly map onto brain activity during speech comprehension.
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Data augmentation is a key element of deep learning pipelines, as it informs the network during training about transformations of the input data that keep the label unchanged.
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e. g. regular speech versus scrambled words, sentences, or paragraphs).
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction
High-quality data accumulation is now becoming ubiquitous in the health domain.
Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements
Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a mathematical model that describes brain tissue via a handful of physiologically-relevant parameters and an algorithm for inverting the model.
Long-range and hierarchical language predictions in brains and algorithms
Predictive coding theory offers a potential explanation to this discrepancy: while deep language algorithms are optimized to predict adjacent words, the human brain would be tuned to make long-range and hierarchical predictions.
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model.
Toward a realistic model of speech processing in the brain with self-supervised learning
These elements, resulting from the largest neuroimaging benchmark to date, show how self-supervised learning can account for a rich organization of speech processing in the brain, and thus delineate a path to identify the laws of language acquisition which shape the human brain.
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Temporal point processes (TPP) are a natural tool for modeling event-based data.
Validation Diagnostics for SBI algorithms based on Normalizing Flows
Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary complex and high-dimensional data distributions.
Evaluating the structure of cognitive tasks with transfer learning
While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, which is not the case in this setting.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
MultiView Independent Component Analysis with Delays
Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals.
Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications
This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited labeled signals in the target dataset.
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability.
