Papers Binary Classification
“Binary Classification” 태그가 달린 논문 3,070편 · 필터 해제
Explainable Deepfake Detection Challenge
Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions. This transition is important for real-world verification settings, …
Explanation GenerationBinary ClassificationImage ClassificationSemantic SimilaritySidewalk Moments: Are Richer Representations Always More Human-Aligned? Evidence from City-Walk Videos
We examine whether richer visual representations yield more human-aligned measures of urban engagement, using 61 first-person city-walk videos from YouTube segmented into over 50,000 ten-second clips and represented acro…
Binary ClassificationRethinking Quantum Continual Learning with Quantum Fisher Information
Quantum continual learning aims to train quantum models on sequential tasks without losing previously learned knowledge. However, variational quantum classifiers (VQCs) are prone to catastrophic forgetting under nonstati…
Binary ClassificationContinual LearningDECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods
Much empirical legal research depends on translating unstructured text into structured variables. In corporate governance research as elsewhere, this translation has traditionally relied on human coding of documents such…
Binary ClassificationModel extractionFrom hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models
We characterize and compare the inherent interpretability offerings of a standard linear model with a single qubit mixed state model for the task of supervised binary classification. A side by side comparison reveals tha…
Quantum Machine LearningBinary ClassificationFeature ImportanceUnit-Independent Low-Rate Wrist GSR Processing for Stress Detection Using Phasic nSCR Features
Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements. In this paper, …
Binary ClassificationA Novel Parallel QCNN Architecture with Efficient Classical Simulability
This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset.…
Binary ClassificationQCNN with Rough Path Signature Kernels
Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A major difficulty arises from the time reparameterization invariance of t…
Time Series ClassificationBinary ClassificationTime Series Analysispath_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a gradient boosting algorithm that automatically discovers predictive labele…
Molecular Property PredictionBinary ClassificationGraph Neural NetworkGeneralized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning
The rapid advancement of large-scale generative models has accelerated the spread of highly deceptive AI-generated images, making generalized synthetic image detection a critical imperative. Existing forensic networks of…
Computational EfficiencyRepresentation LearningBinary ClassificationContrastive LearningClosed-form fractional radial links for elliptical Mahalanobis discriminant analysis
We study binary classification under shared-generator elliptical class-conditional distributions. The log-likelihood ratio is an additive function of the two squared Mahalanobis radii, with radial link $\varphi=\log g$; …
Binary ClassificationRustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities
LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary classification (vulnerable or not), and do …
Vulnerability DetectionBinary ClassificationFast, Parallel, Query-Efficient Binary Classification
We study the fundamental classification problem of computing a separating hyperplane for a binary-labeled dataset of size $n$ with normalized $d$-dimensional features. Letting $Φ\in \mathbb{R}^{n \times d}$ denote the fe…
Binary ClassificationEVAS: Efficient Multimodal Temporal Forgery Localization via Audio-Visual Synergy and Steered Boundary Calibration
The rapid proliferation of artificial intelligence-generated content necessitates reliable multimodal forensics. Beyond video-level binary classification, precisely localizing sparsely distributed forged segments in long…
Representation LearningBinary ClassificationCross-Audit Projection for Model Risk Prediction
For training-data-based model risk prediction, $K$-fold cross-validation~(CV) is widely used to mitigate the well-known over-optimism of the empirical risk and is often regarded as reliable. However, for binary classific…
Breast Cancer DetectionBinary ClassificationForensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence
Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat …
Synthetic Data GenerationBinary ClassificationIntrusion DetectionWhich Metric Reflects the Spelling Rate Accuracy in Event-Related Potential-Based Brain-Computer Interfaces?
For predictive models, the often-reported performance metrics are the loss and accuracy. In synchronous Brain- Computer Interface (BCI) systems, these metrics are informative for most BCI paradigms; however, for Event-Re…
Binary ClassificationFunction-Counting Theory for Low-Dimensional Data Structures
The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend to exhibit, despite their high-dimensional representation. This work at…
Binary ClassificationBeyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning
As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory. In …
Quantum Machine LearningBinary ClassificationDiffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks
Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory. Artificial networks that respect this constraint must coordinate separate excitatory and inhibitory populat…
Representation LearningReinforcement LearningBinary Classification