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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 6051–6100 of 9051 papers

TitleStatusHype
Unsupervised Translation of Emergent Communication—0
Unsupervised User-Based Insider Threat Detection Using Bayesian Gaussian Mixture Models—0
Unsupervised vocal dereverberation with diffusion-based generative models—0
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing—0
CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentation—0
Unveiling Temporal Trends in 19th Century Literature: An Information Retrieval Approach—0
Unveiling the Complexity of Neural Populations: Evaluating the Validity and Limitations of the Wilson-Cowan Model—0
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms—0
UPCS: Unbiased Persona Construction for Dialogue Generation—0
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging—0
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation—0
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation—0
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement—0
Use of Genome Information-Based Potentials to Characterize Human Adaptation—0
Use of Speech Impairment Severity for Dysarthric Speech Recognition—0
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation—0
UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems—0
User Clustering for Rate Splitting using Machine Learning—0
User-Creator Feature Polarization in Recommender Systems with Dual Influence—0
User Fairness in Recommender Systems—0
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems—0
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services—0
User Modeling for Task Oriented Dialogues—0
User-Movement-Robust Virtual Reality Through Dual-Beam Reception in mmWave Networks—0
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity—0
Using a sledgehammer to crack a nut? Lexical diversity and event coreference resolution—0
Using a thousand optimization tasks to learn hyperparameter search strategies—0
Using Audio Books for Training a Text-to-Speech System—0
Using Geometry to Rank Evenness Measures: Towards a Deeper Understanding of Divergence—0
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations—0
Using Known Words to Learn More Words: A Distributional Analysis of Child Vocabulary Development—0
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets—0
Using LLMs as prompt modifier to avoid biases in AI image generators—0
Using LSTMs for climate change assessment studies on droughts and floods—0
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain—0
Using novelty-biased GA to sample diversity in graphs satisfying constraints—0
Using Protected Attributes to Consider Fairness in Multi-Agent Systems—0
Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications—0
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation—0
Using Synthetic Images To Uncover Population Biases In Facial Landmarks Detection—0
Using the Overlapping Score to Improve Corruption Benchmarks—0
Utilizing Differential Evolution into optimizing targeted cancer treatments—0
Utilizing Large Language Models to Synthesize Product Desirability Datasets—0
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images—0
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model—0
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization—0
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors—0
Vagueness in Predicates and Objects—0
Validation Free and Replication Robust Volume-based Data Valuation—0
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