Papers domain classification
“domain classification” 태그가 달린 논문 160편 · 필터 해제
Dynamic Domain Information Modulation Algorithm for Multi-domain Sentiment Analysis
Multi-domain sentiment classification aims to mitigate poor performance models due to the scarcity of labeled data in a single domain, by utilizing data labeled from various domains. A series of models that jointly train…
Classificationdomain classificationHyperparameter OptimizationMulti-Domain Sentiment Classification+2Scaling Crowdsourced Election Monitoring: Construction and Evaluation of Classification Models for Multilingual and Cross-Domain Classification Settings
The adoption of crowdsourced election monitoring as a complementary alternative to traditional election monitoring is on the rise. Yet, its reliance on digital response volunteers to manually process incoming election re…
Classificationdomain classificationInformativenessConvolutional Fourier Analysis Network (CFAN): A Unified Time-Frequency Approach for ECG Classification
Machine learning has revolutionized biomedical signal analysis, particularly in electrocardiogram (ECG) classification. While convolutional neural networks (CNNs) excel at automatic feature extraction, the optimal integr…
Arrhythmia DetectionClassificationdomain classificationECG ClassificationAn In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit Classification
Domain adaptation is an active area of research driven by the growing demand for robust machine learning models that perform well on real-world data. Adversarial learning for deep neural networks (DNNs) has emerged as a …
ClassificationDomain Adaptationdomain classificationimage-classification+1BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis
Multidomain sentiment analysis involves estimating the polarity of an unstructured text by exploiting domain specific information. One of the main issues common to the approaches discussed in the literature is their poor…
Binary ClassificationClassificationdomain classificationSentiment Analysis+1KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces. By analyzing patterns in channel state information (CSI), it is possible to infer human ac…
Action ClassificationAction Classification (1-shot)Action RecognitionDomain Adaptation+6BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition
Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in …
Contrastive Learningdomain classificationnamed-entity-recognitionNamed Entity Recognition+2Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability
Rapid detection of foodborne bacteria is critical for food safety and quality, yet traditional culture-based methods require extended incubation and specialized sample preparation. This study addresses these challenges b…
Domain Adaptationdomain classificationFew-Shot LearningGraph Integration for Diffusion-Based Manifold Alignment
Data from individual observations can originate from various sources or modalities but are often intrinsically linked. Multimodal data integration can enrich information content compared to single-source data. Manifold a…
Data Integrationdomain classificationA Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding
Dialogue State Tracking (DST) is crucial for understanding user needs and executing appropriate system actions in task-oriented dialogues. Majority of existing DST methods are designed to work within predefined ontologie…
Dialogue State TrackingDialogue Understandingdomain classificationQuestion AnsweringWhat happens to diffusion model likelihood when your model is conditional?
Diffusion Models (DMs) iteratively denoise random samples to produce high-quality data. The iterative sampling process is derived from Stochastic Differential Equations (SDEs), allowing a speed-quality trade-off chosen a…
domain classificationmodelSpeech Synthesistext-to-speech+2Enhancing Test Time Adaptation with Few-shot Guidance
Deep neural networks often encounter significant performance drops while facing with domain shifts between training (source) and test (target) data. To address this issue, Test Time Adaptation (TTA) methods have been pro…
Diversitydomain classificationPseudo LabelTest-time AdaptationMIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU
Although Large Language Models(LLMs) can generate coherent and contextually relevant text, they often struggle to recognise the intent behind the human user's query. Natural Language Understanding (NLU) models, however, …
domain classificationIntent DetectionKnowledge DistillationNatural Language Understanding+3Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery
In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction usin…
Binary ClassificationBrain Computer InterfaceBrain DecodingDomain Adaptation+2Harnessing Quantum Support Vector Machines for Cross-Domain Classification of Quantum States
In the present study, we use cross-domain classification using quantum machine learning for quantum advantages to readdress the entanglement versus separability paradigm. The inherent structure of quantum states and its …
Classificationdomain classificationQuantum Machine LearningLeveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extrac…
Dialogue Understandingdomain classificationRetrieval-Augmented Feature Generation for Domain-Specific Classification
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is by expanding the current feature space using existing features a…
Classificationdomain classificationFeature EngineeringInformation Retrieval+5ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models
LLM development involves pre-training a foundation model on massive data, followed by fine-tuning on task-specific data to create specialized experts. Serving these experts can pose significant memory challenges, as load…
Code Generationdomain classificationGPUMathematical Reasoning+2Super-Resolution Analysis for Landfill Waste Classification
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence an…
Classificationdomain classificationSuper-ResolutionHow Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?
Recent breakthroughs in scale have enabled the emergence of powerful generative language models, and the ability to fine-tune these models on various tasks by casting them into prompts or instructions. In this landscape,…
ClassificationDomain Adaptationdomain classificationLanguage Modelling+3