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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 8401–8425 of 9051 papers

TitleStatusHype
The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent SystemsCode0
Low-Cost Self-Ensembles Based on Multi-Branch Transformation and Grouped ConvolutionCode0
MixTEA: Semi-supervised Entity Alignment with Mixture TeachingCode0
Mixture Content Selection for Diverse Sequence GenerationCode0
A Real-time Global Inference Network for One-stage Referring Expression ComprehensionCode0
Diverse Video Captioning by Adaptive Spatio-temporal AttentionCode0
Quantile Regression for Distributional Reward Models in RLHFCode0
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningCode0
Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument GenerationCode0
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual MatchingCode0
Coevolutionary Framework for Generalized Multimodal Multi-objective OptimizationCode0
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
From Distributional to Overton Pluralism: Investigating Large Language Model AlignmentCode0
Sparse Uncertainty-Informed Sampling from Federated Streaming DataCode0
From characters to words: the turning point of BPE mergesCode0
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language RepresentationCode0
Frequency Tracking Features for Data-Efficient Deep Siren IdentificationCode0
Sparsity and adaptivity for the blind separation of partially correlated sourcesCode0
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic HiringCode0
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case StudyCode0
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple ExpertsCode0
Query-adaptive Video Summarization via Quality-aware Relevance EstimationCode0
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target ImputationCode0
Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor NetworksCode0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
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