Papers General Classification
“General Classification” 태그가 달린 논문 14,585편 · 필터 해제
Reinforcement Learning for Neural Model Editing
Editing pretrained neural networks requires specialized algorithms tailored to specific objectives. Designing such algorithms is often time-consuming and demands significant effort. We present an exploratory framework th…
Reinforcement LearningGeneral ClassificationImage ClassificationText ClassificationRethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whet…
Supervised Anomaly DetectionGeneral ClassificationTrack Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
Track Circuits (TC) are the main signalling devices used to detect the presence of a train on a rail track. It has been used since the 19th century and nowadays there are many types depending on the technology. As a gene…
General ClassificationBridging KAN and MLP: MJKAN, a Hybrid Architecture with Both Efficiency and Expressiveness
Kolmogorov-Arnold Networks (KANs) have garnered attention for replacing fixed activation functions with learnable univariate functions, but they exhibit practical limitations, including high computational costs and perfo…
Computational EfficiencyGeneral ClassificationImage ClassificationText ClassificationSpecialized text classification: an approach to classifying Open Banking transactions
With the introduction of the PSD2 regulation in the EU which established the Open Banking framework, a new window of opportunities has opened for banks and fintechs to explore and enrich Bank transaction descriptions wit…
General Classificationtext-classificationText ClassificationUniversal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy
This work invokes the notion of $f$-divergence to introduce a novel upper bound on the Bayes error rate of a general classification task. We show that the proposed bound can be computed by sampling from the output of a p…
ClassificationGeneral Classificationtext-classificationText ClassificationRevisiting MLLMs: An In-Depth Analysis of Image Classification Abilities
With the rapid advancement of Multimodal Large Language Models (MLLMs), a variety of benchmarks have been introduced to evaluate their capabilities. While most evaluations have focused on complex tasks such as scientific…
AttributeClassificationGeneral Classificationimage-classification+3Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives
Objectives: Compare qualitative coding of instruction tuned large language models (IT-LLMs) against human coders in classifying the presence or absence of vulnerability in routinely collected unstructured text that descr…
counterfactualGeneral ClassificationLLM real-life tasksSpecificityRamsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem
This work continues to investigate the link between differentially private (DP) and online learning. Alon, Livni, Malliaris, and Moran (2019) showed that for binary concept classes, DP learnability of a given class impli…
General ClassificationPAC learningCross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition
Human action video recognition has recently attracted more attention in applications such as video security and sports posture correction. Popular solutions, including graph convolutional networks (GCNs) that model the h…
Action ClassificationAction RecognitionGeneral ClassificationVideo RecognitionDiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed tog…
Adversarial RobustnessData AugmentationGeneral ClassificationLarge Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
We consider gradient descent (GD) with a constant stepsize applied to logistic regression with linearly separable data, where the constant stepsize $\eta$ is so large that the loss initially oscillates. We show that GD e…
General ClassificationFacial Beauty Analysis Using Distribution Prediction and CNN Ensembles
Facial Beauty Prediction (FBP) is a computer vision task of quantifying the beauty of a face. Several solutions to this problem have benefitted immensely from the recent developments in deep learning. However, the majori…
Face VerificationFacial Beauty PredictionGeneral ClassificationPrediction+1Improving performance of heart rate time series classification by grouping subjects
Unlike the more commonly analyzed ECG or PPG data for activity classification, heart rate time series data is less detailed, often noisier and can contain missing data points. Using the BigIdeasLab_STEP dataset, which in…
ClassificationGeneral ClassificationTime SeriesTime Series ClassificationExplainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improvi…
General ClassificationIntent Detectionnamed-entity-recognitionNamed Entity Recognition+4Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices
The recent surge of interest surrounding Multimodal Neural Networks (MM-NN) is attributed to their ability to effectively process and integrate multiscale information from diverse data sources. MM-NNs extract and fuse fe…
General ClassificationMultimodal Text and Image ClassificationNeural Architecture SearchSTRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Preference-based reinforcement learning (PbRL) promises to learn a complex reward function with binary human preference. However, such human-in-the-loop formulation requires considerable human effort to assign preference…
General Classificationreinforcement-learningUnlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence
Nowadays, there is growing interest in applying Artificial Intelligence (AI) on board Earth Observation (EO) satellites for time-critical applications, such as natural disaster response. However, the unavailability of ra…
Disaster ResponseEarth ObservationGeneral ClassificationCEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering
Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, …
ClusteringDeep ClusteringGeneral ClassificationLanguage Modeling+4VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of r…
DisentanglementDomain GeneralizationFew-Shot Image ClassificationGeneral Classification+7