Papers Early Classification
“Early Classification” 태그가 달린 논문 38편 · 필터 해제
CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration
Machine Learning (ML) is used to tackle various tasks, such as disease classification and prediction. The effectiveness of ML models relies heavily on having large amounts of complete data. However, healthcare data is of…
ClassificationEarly ClassificationVisual Image Reconstruction from Brain Activity via Latent Representation
Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces th…
Early ClassificationImage ReconstructionZero-shot GeneralizationXMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics
Hand kinematics can be measured in Human-Computer Interaction (HCI) with the intention to predict the user's intention in a reach-to-grasp action. Using multiple hand sensors, multivariate time series data are being capt…
Early ClassificationTime SeriesLearning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test
Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in finite horizon scenarios, where input l…
Density Ratio EstimationEarly Classificationml_edm package: a Python toolkit for Machine Learning based Early Decision Making
\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing researchers an easy way to implement their own t…
Decision MakingEarly ClassificationTime SeriesEarly Classification of Time Series: Taxonomy and Benchmark
In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early …
ClassificationEarly ClassificationTime SeriesNon-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO
With 95% of Internet traffic now encrypted, an effective approach to classifying this traffic is crucial for network security and management. This paper introduces ECHO -- a novel optimization process for ML/DL-based enc…
AllClassificationEarly ClassificationHyperparameter Optimization+1Representation Learning of Tangled Key-Value Sequence Data for Early Classification
Key-value sequence data has become ubiquitous and naturally appears in a variety of real-world applications, ranging from the user-product purchasing sequences in e-commerce, to network packet sequences forwarded by rout…
Early ClassificationRepresentation LearningSecond-order Confidence Network for Early Classification of Time Series
Time series data are ubiquitous in a variety of disciplines. Early classification of time series, which aims to predict the class label of a time series as early and accurately as possible, is a significant but challengi…
Early ClassificationTime SeriesChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors
Multivariate time series data are ubiquitous in the application of machine learning to problems in the physical sciences. Chemiresistive sensor arrays are highly promising in chemical detection tasks relevant to industri…
BenchmarkingClassificationEarly ClassificationSurvey+2Multimodal Identification of Alzheimer's Disease: A Review
Alzheimer's disease is a progressive neurological disorder characterized by cognitive impairment and memory loss. With the increasing aging population, the incidence of AD is continuously rising, making early diagnosis a…
DiagnosticEarly ClassificationEEGElectroencephalogram (EEG)Early-Exit with Class Exclusion for Efficient Inference of Neural Networks
Deep neural networks (DNNs) have been successfully applied in various fields. In DNNs, a large number of multiply-accumulate (MAC) operations are required to be performed, posing critical challenges in applying them in r…
ClassificationEarly ClassificationAutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification
Deep learning (DL) has been successfully applied to encrypted network traffic classification in experimental settings. However, in production use, it has been shown that a DL classifier's performance inevitably decays ov…
ClassificationEarly ClassificationNeural Architecture SearchTraffic ClassificationMultivariate Time Series Early Classification Across Channel and Time Dimensions
Nowadays, the deployment of deep learning models on edge devices for addressing real-world classification problems is becoming more prevalent. Moreover, there is a growing popularity in the approach of early classificati…
ClassificationEarly ClassificationReinforcement Learning (RL)Time Series+1Dynamic Perceiver for Efficient Visual Recognition
Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for ``easy'' samples can be generated at earlier …
Action RecognitionClassificationCPUEarly Classification+5Early Classifying Multimodal Sequences
Often pieces of information are received sequentially over time. When did one collect enough such pieces to classify? Trading wait time for decision certainty leads to early classification problems that have recently gai…
ClassificationEarly ClassificationToward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification
Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series. Conventional SDRE algorithms can fail to estimate DRs precisely due to the internal o…
ClassificationDensity Ratio EstimationEarly Classificationregression+2Stop&Hop: Early Classification of Irregular Time Series
Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their patients' outcomes by accurately predic…
Early ClassificationGeneral ClassificationIrregular Time SeriesTime Series+1When to Classify Events in Open Times Series?
In numerous applications, for instance in predictive maintenance, there is a pression to predict events ahead of time with as much accuracy as possible while not delaying the decision unduly. This translates in the optim…
ClassificationDecision MakingEarly ClassificationTime Series+1Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves
In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-…
ClassificationDeep AttentionDomain AdaptationEarly Classification+2