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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 4226–4250 of 9051 papers

TitleStatusHype
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
A deep-learning-based approach for fast and robust steel surface defects classification—0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—0
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data—0
DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections—0
A Classifier-free Ensemble Selection Method based on Data Diversity in Random Subspaces—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks—0
Beyond Relevance: An Adaptive Exploration-Based Framework for Personalized Recommendations—0
An Empirical Study of Group Conformity in Multi-Agent Systems—0
Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making—0
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies—0
Importance-Driven Deep Learning System Testing—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
An Empirical Study of Finding Similar Exercises—0
Dimensions of Diversity in Human Perceptions of Algorithmic Fairness—0
A Deep Generative Model for Feasible and Diverse Population Synthesis—0
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets—0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
Implementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)—0
DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models—0
DiMA: Sequence Diversity Dynamics Analyser for Viruses—0
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation—0
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules—0
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