Novel graph framework detects early Alzheimer's using brain structure similarity.
problem Early detection of Alzheimer's disease is challenging due to irreversible neurodegenerative process.
method Combines inter-subject similarity and intra-subject variability within a graph of brain structures grading.
result Competitive performance compared to state-of-the-art methods.
New method aligns brain surfaces based on functional signatures.
problem Inter-individual variability in neuroimaging data.
method Fused Unbalanced Gromov-Wasserstein (FUGW) based on Optimal Transport.
result FUGW significantly increases between-subject correlation of activity.
New framework infers sparse inter-subject connections from dense intra-data.
problem Inferring sparse inter-subject connections from dense intra-data in neuroscience.
method Gaussian graphical models, alternative parameter estimation, and chord procedure for inference.
result Asymptotic consistency of estimator and inference method without sparsity assumption.
New model extracts shared brain activity patterns from fMRI data.
problem Challenges in aggregating multi-subject fMRI data due to variability.
method Shared Gaussian Process Factor Analysis (S-GPFA) incorporating temporal information.
result Model reveals ground truth latent structures and replicates experimental performance.
This paper addresses the problem of emotion recognition from physiological signals. Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. The inter-subject variability and the personalization effect are thoroughly investigated, through trial-based and su…
Brain signal variability in the measurements obtained from different subjects during different sessions significantly deteriorates the accuracy of most brain-computer interface (BCI) systems. Moreover these variabilities, also known as inter-subject or inter-session variabilities, require lengthy calibration sessions b…
Proposes a framework for causal inference with processed outcomes in biomedical research.
problem Impact of intra-subject processing on inter-subject statistical inference in biomedical research.
method Semiparametric framework with multiply robust estimators and step-down procedure for high-dimensional inference.
result Superior performance of the proposed approach demonstrated through simulations and application to autism research.
New algorithm detects seizures more accurately across subjects.
problem Inter-subject variability in brain signal analysis.
method Clustering covariance matrices on a Riemannian manifold, unsupervised selection of relevant subjects, SVM classifier training.
result Accuracy increased from 86.83% to 89.84% and specificity from 87.38% to 89.64%.
Improves data efficiency for estimating mutual information.
problem Estimating mutual information from limited data.
method Developed a Data-Efficient MINE Estimator (DEMINE) and Meta-DEMINE.
result Significantly improved data efficiency in estimating mutual information.
Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts whose cortical position is highly variable. As such, most ali…
New method learns disentangled representations from ECG data for VT origin classification.
problem Challenges in learning subject-specific models from ECG data due to inter-subject variations.
method Conditional Variational Autoencoder (VAE) with maximum mean discrepancy regularization and contrastive regularization.
result Demonstrated efficacy in classifying VT origin segments compared to standard VAE.
Deep-learning method improves sEMG-based gesture recognition accuracy.
problem Inter-session and inter-subject variances in sEMG-based gesture recognition.
method Recurrent Neural Networks and Domain Adaptation method.
result Our approach outperforms state-of-the-art methods in recognition accuracy.
New metric and method for sEMG-based gesture recognition under domain shifts.
problem Measuring and adapting to domain divergence in sEMG-based gesture recognition.
method Probability distribution-based metric, 2-stage autoregressive RNN architecture.
result Improved autoregressive, RNN-based architecture enhances performance.
Study uses machine learning to detect pain from brain signals.
problem No validated objective measure of pain exists.
method Multi-task multiple kernel learning for personalized pain recognition.
result Supports use of fNIRS and machine learning for objective pain detection.
Geometry-aware models improve cross-subject EEG decoding accuracy.
problem Strong inter-subject variability in motor imagery decoding.
method Discriminative Congruence Transform (DCT), Deep Linear DCT (DLDCT), Deep DCT-UNet (DDCT-UNet).
result Improves transductive cross-subject accuracy by 2-3%.
Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of t…
In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity. Typically, data is collected across a cohort of subjects and the scientific objectives consist of estimating population and subject-specific g…
The study recovers airflow from thoracic and abdominal movements using advanced signal processing.
problem Challenges in measuring airflow from thoracic and abdominal movements using small, inexpensive devices.
method Synchrosqueezing transform and locally stationary Gaussian process regression.
result Accurate prediction of airflow achieved in both normal sleep and anesthesia transition cases.
Unsupervised model predicts facial attractiveness with high accuracy.
problem Capturing the complexity of facial attractiveness through machine learning.
method Infer probabilistic models of facial preferences using Maximum Entropy and neural networks.
result High prediction accuracy in gender classification of sculpting subjects.
Paper tackles deep learning confounding factors, learns unseen factors.
problem Learning from data with unknown and potentially infinite confounding factors.
method Combines deep generative models with Bayesian non-parametric factor models (Indian Buffet Process).
result Model can learn from data with unknown and potentially infinite confounding factors.
1-D CNNs classify pupil size variations in scotopic conditions.
problem Handling wide inter-subjects variability in pupil analysis.
method 1-D Convolutional Neural Networks applied directly to raw pupil size data.
result 1-D CNNs provide high accuracy in classifying short-range pupil size sequences.
Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse inference ta…
Proposes a new framework for EEG-based BCIs without adversarial learning.
problem High intra- and inter-subject variabilities in EEG data.
method Mutual information-driven deep learning approach to learn class-relevant and subject-invariant feature representations.
result Effective in learning class-relevant and subject-invariant feature representations without adversarial learning.
Study explores how dataset breadth and depth affect Siamese Neural Network performance.
problem Impact of dataset breadth and depth on Siamese Neural Network performance.
method Experiments with three keystroke datasets varying breadth and depth factors.
result Increasing dataset breadth improves model performance, while depth's impact varies by dataset type.
Machine learning improves accuracy of running gait event detection from tibial acceleration.
problem Accurate detection of running gait events from tibial acceleration data.
method Structured machine learning models compared to heuristic methods.
result Structured recurrent neural network model offers most accurate estimation of gait events.
GSimCNN predicts graph similarity using CNNs, outperforming existing methods.
problem Challenging pairwise graph similarity computation due to NP-hardness.
method Graph Edit Distance (GED) as core metric, GSimCNN (Convolutional Neural Networks).
result State-of-the-art performance on graph similarity search.
Enhances clustering performance by integrating tensor similarity.
problem Noise contamination and imbalance in samples or features hinder accurate clustering.
method Proposes a high-order similarity matrix from tensor similarity, which captures spatial information and complements pairwise similarity.
result The proposed IPS2 method significantly outperforms previous similarity-based methods on real-world datasets.
Introduces CHL, a new loss function for continuous similarity learning.
problem Binary similarity learning limitations.
method CHL is a novel loss function that generalizes histogram loss to continuous similarities.
result CHL solves a wider range of tasks including similarity learning, representation learning, and data visualization.
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small sub…
Deconfounds neural network representation similarity metrics to improve consistency and accuracy.
problem Confounding by population structure in similarity metrics like RSA and CKA.
method Covariate adjustment regression to adjust for confounders.
result Improves detection of semantically similar neural networks and consistency in transfer learning.
Quantum networks learn task-dependent asymmetric similarity measures.
problem Challenges of conventional distance functions in capturing meaningful similarity.
method GQSim: Quantum networks for learning task-dependent (a)symmetric similarity.
result Quantum similarity measures extract salient features and achieve theoretically guaranteed performance.
Defines a similarity measure for classification distributions.
problem Measuring similarity between classification distributions.
method Proposes task similarity, a novel measure quantifying performance of source distributions on target distributions.
result Empirical task similarity correlates with transfer efficiency and semantic similarity of source distributions.
Proposes neural similarity for CNNs to enhance flexibility and performance.
problem Limited flexibility of inner product-based convolution in CNNs.
method Introduces neural similarity as a learnable parametric similarity measure, and proposes NSL for adaptive learning from data.
result Dynamic neural similarity improves flexibility and performance in visual recognition and few-shot learning.
In this paper, we investigate the similarity transformations in the Minkowski-n space. We study the geometric invariants of non-null curves under the similarity transformations. Besides, we extend the fundamental theorem for a non-null curve according to a similarity motion. We determine all non-null self-similar curve…
Paper proposes a new framework for learning discriminative similarity for clustering and semi-supervised learning.
problem The importance of pairwise similarity for clustering and semi-supervised learning performance.
method Proposes a novel discriminative similarity learning framework that learns from hypothetical labelings and minimizes generalization error.
result Discriminative similarity learned from hypothetical labelings can improve clustering and semi-supervised learning performance.
Automates similarity measure construction from data.
problem Defining similarity measures analytically is challenging.
method Applies machine learning to learn similarity measures from data.
result Data-driven similarity measure outperforms state of the art methods.
Modified cosine distance improves similarity performance in data with variance and correlation.
problem Limitations of traditional cosine similarity in random variable spaces with variance and correlation.
method Proposed a variance-adjusted cosine distance metric to overcome limitations of traditional cosine similarity.
result Modified cosine distance shows 100% test accuracy in KNN model on the Wisconsin Breast Cancer Dataset.
Advocates Tversky's model for image similarity learning.
problem Learning Tversky similarity measures from image data.
method Computational approach using Tversky's ratio model.
result Performs well compared to existing methods on image datasets.
Paper presents a new trie for integer sketches to improve similarity searches.
problem Efficient similarity searches on integer sketches.
method Introduces a novel b-bit sketch trie that leverages succinct data structures. result Significantly improves search time and space-efficiency of similarity searches.
WIPS optimizes inner product weights to approximate various similarities.
problem Learning high-quality node representations and accurate similarities.
method Weighted inner product similarity (WIPS) with adjustable weights.
result WIPS can approximate arbitrary general similarities including positive definite and indefinite kernels.
SIPS extends graph embedding by approximating more types of similarities.
problem Graph embedding's limitation in approximating certain types of similarities.
method Shifted inner-product similarity (SIPS) with bias terms.
result SIPS can approximate PD and CPD similarities, improving graph embedding performance.
Method measures weight similarity in neural networks using normalization and statistical inference.
problem Quantifying weight similarity in non-convex neural networks.
method Chain normalization rule and hypothesis-training-testing statistical inference.
result Weights of identical neural networks converge to similar local solutions.
We prove that the only self-similar surfaces of Euclidean 3-space which are foliated by circles are the self-similar surfaces of revolution discovered by S. Angenent and that the only ruled, self-similar surfaces are the cylinders over planar self-similar curves.
Improves confidence calibration in neural networks by smoothing labels based on class similarity.
problem Improving confidence calibration in deep neural networks for safety-critical applications.
method Proposes a novel label smoothing technique where label values are based on similarities with the reference class, using different similarity measurements.
result Consistently outperforms state-of-the-art calibration techniques on various datasets and network architectures.
CatSIM measures image similarity robustly to small changes.
problem Measuring similarity between images, especially with small perturbations.
method Uses structural similarity image quality paradigm, robust to small location changes.
result Structural similarity between images rated higher when not entirely overlapping.
Proposes DNN-based speaker embedding correlated with subjective inter-speaker similarity for speech synthesis.
problem Inadequate speaker representation for open speakers not in training data.
method Two training algorithms using inter-speaker similarity matrices: similarity vector embedding and similarity matrix embedding.
result Proposed algorithms learn speaker embedding highly correlated with subjective inter-speaker similarity.
This work shows cosine similarity is equivalent to Pearson correlation for word vectors, but not all vectors are suitable for cosine.
problem The use of cosine similarity for semantic textual similarity is often taken for granted, despite its limitations.
method Characterized cases where Pearson correlation is unfit and introduced rank correlation as an alternative.
result Pearson correlation is equivalent to cosine similarity for many word vectors but not all, and rank correlation can improve performance.
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
problem Measuring place function similarity at fine spatial granularity.
method Trajectory embedding to reduce dimensions and measure similarity of place functions.
result Embedding similarity can be a metric proxy for place functions at fine spatial granularity.