SOTAVerified

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

Showing 86518675 of 9051 papers

TitleStatusHype
Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary EvaluationCode0
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing SymmetriesCode0
Reinforcement Learning for Topic ModelsCode0
Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn DynamicsCode0
Dissecting CLIP: Decomposition with a Schur Complement-based ApproachCode0
Disentangled Representation for Diversified RecommendationsCode0
Unsupervised Question Answering via Answer DiversifyingCode0
Discrete Modeling of Multi-Transmitter Neural Networks with Neuron CompetitionCode0
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?Code0
Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approachCode0
Discovering the Elite Hypervolume by Leveraging Interspecies CorrelationCode0
Relation-Rich Visual Document Generator for Visual Information ExtractionCode0
The Shrinking Landscape of Linguistic Diversity in the Age of Large Language ModelsCode0
Fair Summarization: Bridging Quality and Diversity in Extractive SummariesCode0
Tregs self-organize into a "computing ecosystem" and implement a sophisticated optimization algorithm for mediating immune responseCode0
Multi-trait User Simulation with Adaptive Decoding for Conversational Task AssistantsCode0
CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous DrivingCode0
Fair and accurate age prediction using distribution aware data curation and augmentationCode0
Channel Augmented Joint Learning for Visible-Infrared RecognitionCode0
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific VotersCode0
Fairness and Diversity in Recommender Systems: A SurveyCode0
Multi-view Deep Subspace Clustering NetworksCode0
Multi-view Intent Disentangle Graph Networks for Bundle RecommendationCode0
Challenges of Generating Structurally Diverse GraphsCode0
Discovering Sensorimotor Agency in Cellular Automata using Diversity SearchCode0
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