This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire collection of hidden layer node states from the training dataset is …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
Trend · papers per month
48 results for “state classification”
KFHE uses Kalman filters to improve ensemble classification accuracy.
problem Improving multi-class ensemble classification accuracy.
method KFHE treats ensemble training as a state estimation problem using Kalman filters.
result KFHE outperforms state-of-the-art algorithms in noisy and clean datasets.
TSSC images enhance chaotic signal classification using ConvNets.
problem Classifying chaotic signals accurately and robustly.
method Triad State Space Construction (TSSC) for image encoding, Convolutional Neural Network (ConvNet) for classification.
result TSSC-ConvNet achieves high accuracy and robustness in chaotic signal classification.
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
Improves NILM with multi-label SRC, outperforming state-of-the-art.
problem Non-intrusive load monitoring (NILM) for energy disaggregation.
method Modified multi-label sparse representation based classification (SRC).
result Significant improvement over state-of-the-art techniques with minimal training data.
Improved time series classification with GRU-FCN model.
problem Time series classification challenges.
method Hybrid LSTM-GRU model for univariate time series classification.
result GRU-FCN model outperforms state-of-the-art models.
ROCKET speeds up time series classification without sacrificing accuracy.
problem High computational complexity and intractability of existing time series classification methods.
method Simple linear classifiers using random convolutional kernels.
result Achieves state-of-the-art accuracy with significantly reduced computational expense.
Classification of ground state solutions to critical Dirac equation on spheres.
problem Classifying ground state solutions of the critical Dirac equation.
method Exploiting conformal covariance and relating to the Yamabe equation.
result Ground state solutions are given by Killing spinors up to conformal diffeomorphisms.
NSC classifies hybrid system states for time-bounded reachability, achieving high accuracy with minimal false negatives.
problem Classifying states in hybrid systems for time-bounded reachability.
method Neural State Classification using Deep Neural Networks.
result Achieved 99.25% to 99.98% accuracy with false-negative rates reduced to 0.0015 to 0 after tuning.
Structured state space models improve ECG classification and reveal new insights.
problem Improving ECG analysis through deep learning.
method Applying structured state space models to capture long-term dependencies in ECG data.
result SSMs lead to significant improvements in ECG classification over current state-of-the-art.
Bayesian topological learning improves EEG signal analysis for brain state classification.
problem Challenges in classifying and analyzing noisy, nonlinear, nonstationary EEG signals.
method Persistent homology with Bayesian framework to track topological features and incorporate prior knowledge.
result Bayesian topological learning outperforms existing methods for noisy EEG classification.
Divides state space into regions with identical term structure shapes.
problem Classifying term structure shapes in the two-factor Vasicek model.
method Using envelopes and winding numbers to divide and classify the state space.
result Nearly complete classification of parameter space regarding term structure shapes.
Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learningastro-ph.IM
Deep learning predicts nuclear equation of state from rotating core collapse GW signals.
problem Classifying the nuclear equation of state from rotating core collapse gravitational wave signals.
method Employed deep convolutional neural networks to classify visual and temporal patterns in GW signals.
result Up to 97% correct classifications of nuclear equation of state in the test set.
r-STSF improves TSC accuracy and interpretability.
problem Lack of interpretability in state-of-the-art TSC methods.
method Randomized-Supervised Time Series Forest (r-STSF) using interval-based approach and ensemble of randomized trees.
result r-STSF achieves state-of-the-art accuracy and enables interpretability.
A new method combines topological features with graph convolutional networks for improved paper classification.
problem Classifying papers based on their content and structure.
method Combining topological features of nodes with information propagation through Graph Convolutional Networks (GCN).
result The method improves classification accuracy on CiteSeer and Cora datasets, matching or exceeding text-based classification results.
Stochastic encoding improves gender classification of brain networks from UK Biobank data.
problem Complexity and bias in interpreting deep learning models of brain connectivity.
method Stochastic encoding in ensemble of CNNs, multivariate balancing algorithm.
result AUROC of 0.8459, with resting-state data more accurate than task data.
PEOC uses policy entropy to detect untrained states in RL.
problem Detecting untrained states in reinforcement learning for safety.
method Policy entropy based one-class classifier.
result PEOC is highly competitive and reliable.
A new dataset for few-shot relation classification challenges current models.
problem Few-shot relation classification is an open problem requiring further research.
method Adapted state-of-the-art few-shot learning methods for relation classification.
result Current models struggle with relation classification, especially compared to humans.
Quantum machine learning classification depends on mutual informations between state and parameter spaces.
problem Generalization in quantum machine learning models.
method Link between quantum machine learning and quantum hypothesis testing, using mutual informations.
result Quantum classifier accuracy and generalization depend on mutual informations between state and parameter spaces.
Improved patent classification using fine-tuned BERT model.
problem Classifying large patent datasets efficiently and accurately.
method Fine-tuning a pre-trained BERT model on patent claims.
result Outperforms state-of-the-art methods by 20%.
Proposes interpretable time series classification through extracted features.
problem Interpretable time series classification in complex problems.
method Extracts features from time series to improve interpretability of traditional classifiers.
result No statistically significant differences in accuracy compared to state-of-the-art models.
Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometrycs.LG
The paper analyzes and proposes a new stopping criterion for recursive Bayesian classification.
problem Limitations of conventional stopping criteria in recursive Bayesian classification.
method Geometric interpretation of state posterior progression and analysis of conventional criteria.
result Proposes a new stopping criterion to overcome limitations of conventional methods.
Pattern recognition is a central topic in Learning Theory with numerous applications such as voice and text recognition, image analysis, computer diagnosis. The statistical set-up in classification is the following: we are given an i.i.d. training set where represents a feature…
New method explains time series classification by assessing causal effects.
problem Understanding machine learning model decisions in time series classification.
method Model-agnostic causal attribution method using diffusion models.
result Causal attributions differ from associational ones, highlighting risks.
GeoStat simplifies time series classification with fast, intuitive features.
problem Efficiently classify time series data without high computational costs.
method GeoStat representations based on differential geometric statistics.
result Simple KNN and SVM classifiers achieve state-of-the-art performance.
Deep learning model improves EEG seizure classification accuracy.
problem Manual EEG analysis by neurologists is labor-intensive and prone to errors.
method Integrates IndRNN with dense structure and attention mechanism for temporal and spatial feature extraction.
result Average sensitivity, specificity, and precision of 88.80%, 88.60%, and 88.69% on noisy CHB-MIT data set.
In this paper we describe the problem of painter classification, and propose a novel approach based on deep convolutional autoencoder neural networks. While previous approaches relied on image processing and manual feature extraction from paintings, our approach operates on the raw pixel level, without any preprocessin…
Enhances few-shot image classification using unlabelled examples.
problem Few-shot image classification with limited labeled data.
method Transductive meta-learning combining soft k-means clustering and neural feature extractor.
result State-of-the-art performance on Meta-Dataset, mini-ImageNet, and tiered-ImageNet benchmarks.
Tensor networks improve image classification but require more expressive states.
problem Understanding why tensor networks work in image classification.
method Investigated entanglement properties of tensor network models for supervised image classification.
result Tensor networks can learn states that are robustly entangled, suggesting long-range entanglement is not essential.
Classification of curves up to affine transformation in a finite dimensional space was studied by some different methods. In this paper, we achieve the exact formulas of affine invariants via the equivalence problem and in the view of Cartan's lemma and then, state a necessary and sufficient condition for classificatio…
UGformer uses transformers to learn graph representations.
problem Graph representation learning for various tasks.
method UGformer is a transformer-based GNN model that samples or considers all neighbors for each node.
result UGformer achieves state-of-the-art accuracy on graph classification and text classification tasks.
DCTN uses tensor networks for image classification, achieving state-of-the-art results.
problem Improving image classification accuracy with deep neural networks.
method Developed a novel deep convolutional tensor network (DCTN) based on Entangled plaquette states (EPS).
result DCTN achieves state-of-the-art results on MNIST and FashionMNIST but overfits on CIFAR10.
Researchers compute Bayes error for classification models using normalizing flows.
problem Evaluating the inherent difficulty of classification problems.
method Invertible transformations and Gaussian base distributions to compute Bayes error.
result State-of-the-art models can achieve near-optimal accuracy but not always.
PiNet improves graph classification efficiency and accuracy.
problem Graph level classification challenges.
method Attention-based pooling mechanism for graph convolution operations.
result Superior performance and high sample efficiency.
HAXMLNet tackles extreme multi-label text classification with hierarchical attention.
problem Tagging each text with relevant labels from an extreme-scale label set.
method Proposes a hierarchical structure with multi-label attention for efficient and effective XMTC.
result HAXMLNet achieves competitive performance compared to state-of-the-art methods.
Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems…
A hybrid K-NN and SVM technique improves classification accuracy.
problem Improving classification accuracy in pattern recognition.
method Discriminative nearest neighbour classification combined with SVM.
result The hybrid technique outperforms state-of-the-art methods.
New model for time series classification from single example.
problem Classifying time series patterns from limited data.
method Developed a Hidden semi-Markov Model with variable state duration.
result Different representations of state duration have distinct strengths and weaknesses.
Deep neural networks outperform traditional ensemble methods in time series classification.
problem Deep learning models struggle to match traditional ensemble methods in time series classification.
method Developed an ensemble of 60 deep learning models to improve time series classification performance.
result The proposed Neural Network Ensemble (NNE) outperforms current state-of-the-art methods.
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
problem Hierarchical multi-label classification with class hierarchy constraints.
method Exploits class hierarchy to produce coherent predictions for multi-label classification.
result C-HMCNN(h) outperforms state-of-the-art models in HMC classification.
Develops STC for sequential data with missing labels.
problem Learning from partially labeled and unsegmented sequential data.
method Introduces Star Temporal Classification (STC) using a star token and GTN framework.
result Recover most of supervised baseline performance with up to 70% missing labels.
New methods for handling time-varying label noise in time series classification.
problem Temporal label noise in time series classification tasks.
method Proposed methods to estimate temporal label noise function directly from data.
result Our methods lead to state-of-the-art performance under diverse types of temporal label noise.
Paper classifies economic states and optimizes portfolios for stagflationary environments.
problem Economic uncertainty and stagflationary conditions.
method Mathematical techniques for analyzing multivariate time series, economic driver analysis, self-similarity identification, and portfolio optimization.
result Constructs economic state classifications and computes economic state integrals.
Few-shot image classification is improved by correcting CNNs' texture bias.
problem Few-shot image classification performance is hindered by CNNs' texture bias.
method Corrected CNNs' texture bias using a simpler method than state-of-the-art approaches.
result State-of-the-art performance on miniImageNet task achieved.
AclNet improves audio classification with high accuracy and reduced complexity.
problem Efficiently classifying audio data with high accuracy.
method Proposed AclNet architecture with data augmentation and regularization.
result Achieved state-of-the-art performance on ESC-50 corpus with 85.65% accuracy.
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a c…
This paper introduces matrix product state (MPS) decomposition as a new and systematic method to compress multidimensional data represented by higher-order tensors. It solves two major bottlenecks in tensor compression: computation and compression quality. Regardless of tensor order, MPS compresses tensors to matrices …
Paper presents MTTDSC for better target-specific sentiment classification.
problem Improving accuracy in detecting and aggregating sentiments towards specific targets in social media.
method MTTDSC uses a multi-task learning approach with an auxiliary task for passage-level sentiment classification and a main task for target-specific sentiment classification.
result MTTDSC outperforms state-of-the-art baselines in sentiment classification.