Papers Multi-class Classification
“Multi-class Classification” 태그가 달린 논문 1,009편 · 필터 해제
Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
We investigate Stochastic Mirror Descent (SMD) with matrix parameters and vector-valued predictions, a framework relevant to multi-class classification and matrix completion problems. Focusing on the overparameterized re…
Multi-class ClassificationThe Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
A variety of widely used optimization methods like SignSGD and Muon can be interpreted as instances of steepest descent under different norm-induced geometries. In this work, we study the implicit bias of mini-batch stoc…
Multi-class ClassificationStochastic OptimizationVulReaD: Knowledge-Graph-guided Software Vulnerability Reasoning and Detection
Software vulnerability detection (SVD) is a critical challenge in modern systems. Large language models (LLMs) offer natural-language explanations alongside predictions, but most work focuses on binary evaluation, and ex…
Multi-class ClassificationVulnerability DetectionBinary ClassificationMalware Detection Through Memory Analysis
This paper summarizes the research conducted for a malware detection project using the Canadian Institute for Cybersecurity's MalMemAnalysis-2022 dataset. The purpose of the project was to explore the effectiveness and e…
Multi-class ClassificationBinary ClassificationMalware DetectionMultimodal Visual Surrogate Compression for Alzheimer's Disease Classification
High-dimensional structural MRI (sMRI) images are widely used for Alzheimer's Disease (AD) diagnosis. Most existing methods for sMRI representation learning rely on 3D architectures (e.g., 3D CNNs), slice-wise feature ex…
Multi-class ClassificationRepresentation LearningClinNet: Evidential Ordinal Regression with Bilateral Asymmetry and Prototype Memory for Knee Osteoarthritis Grading
Knee osteoarthritis (KOA) grading based on radiographic images is a critical yet challenging task due to subtle inter-grade differences, annotation uncertainty, and the inherently ordinal nature of disease progression. C…
Multi-class ClassificationImproved Balanced Classification with Theoretically Grounded Loss Functions
The balanced loss is a widely adopted objective for multi-class classification under class imbalance. By assigning equal importance to all classes, regardless of their frequency, it promotes fairness and ensures that min…
Multi-class ClassificationFundamental Novel Consistency Theory: $H$-Consistency Bounds
In machine learning, the loss functions optimized during training often differ from the target loss that defines task performance due to computational intractability or lack of differentiability. We present an in-depth s…
Multi-class ClassificationBinary ClassificationTheory and Algorithms for Learning with Multi-Class Abstention and Multi-Expert Deferral
Large language models (LLMs) have achieved remarkable performance but face critical challenges: hallucinations and high inference costs. Leveraging multiple experts offers a solution: deferring uncertain inputs to more c…
Multi-class ClassificationFederated Learning With L0 Constraint Via Probabilistic Gates For Sparsity
Federated Learning (FL) is a distributed machine learning setting that requires multiple clients to collaborate on training a model while maintaining data privacy. The unaddressed inherent sparsity in data and models oft…
Multi-Label ClassificationMulti-class ClassificationFederated LearningGHaLIB: A Multilingual Framework for Hope Speech Detection in Low-Resource Languages
Hope speech has been relatively underrepresented in Natural Language Processing (NLP). Current studies are largely focused on English, which has resulted in a lack of resources for low-resource languages such as Urdu. As…
Multi-class ClassificationHope Speech DetectionBinary ClassificationBinary Kernel Logistic Regression: a sparsity-inducing formulation and a convergent decomposition training algorithm
Kernel logistic regression (KLR) is a widely used supervised learning method for binary and multi-class classification, which provides estimates of the conditional probabilities of class membership for the data points. U…
Multi-class ClassificationAnother Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics
Machine-learning techniques are essential in modern collider research, yet their probabilistic outputs often lack calibrated uncertainty estimates and finite-sample guarantees, limiting their direct use in statistical in…
Multi-class ClassificationAnomaly DetectionRandomized multi-class classification under system constraints: a unified approach via post-processing
We study the problem of multi-class classification under system-level constraints expressible as linear functionals over randomized classifiers. We propose a post-processing approach that adjusts a given base classifier …
Multi-class ClassificationIntrusion Detection in Internet of Vehicles Using Machine Learning
The Internet of Vehicles (IoV) has evolved modern transportation through enhanced connectivity and intelligent systems. However, this increased connectivity introduces critical vulnerabilities, making vehicles susceptibl…
Multi-class ClassificationIntrusion DetectionSelf-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging
Prenatal ultrasound is the cornerstone for detecting congenital anomalies of the kidneys and urinary tract, but diagnosis is limited by operator dependence and suboptimal imaging conditions. We sought to assess the perfo…
Multi-class ClassificationSelf-Supervised LearningRepresentation LearningAnomaly ClassificationCIEGAD: Cluster-Conditioned Interpolative and Extrapolative Framework for Geometry-Aware and Domain-Aligned Data Augmentation
In practical deep learning deployment, the scarcity of data and the imbalance of label distributions often lead to semantically uncovered regions within the real-world data distribution, hindering model training and caus…
Multi-class ClassificationData AugmentationFrom Graphs to Gates: DNS-HyXNet, A Lightweight and Deployable Sequential Model for Real-Time DNS Tunnel Detection
Domain Name System (DNS) tunneling remains a covert channel for data exfiltration and command-and-control communication. Although graph-based methods such as GraphTunnel achieve strong accuracy, they introduce significan…
Multi-class ClassificationGraph GenerationFrom Regression to Classification: Exploring the Benefits of Categorical Representations of Energy in MLIPs
Density Functional Theory (DFT) is a widely used computational method for estimating the energy and behavior of molecules. Machine Learning Interatomic Potentials (MLIPs) are models trained to approximate DFT-level energ…
Multi-class ClassificationMitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarization
Recent advancements in large language models (LLMs) have shown their potential across both general and domain-specific tasks. However, there is a growing concern regarding their lack of sensitivity, factual incorrectness…
Multi-class Classification