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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 8676–8700 of 9051 papers

TitleStatusHype
A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem SolvingCode0
MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse InstructionsCode0
Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through RecommendationsCode0
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language GenerationCode0
Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question AnsweringCode0
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimalityCode0
Music Playlist Title Generation Using Artist InformationCode0
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data SynthesisCode0
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup SegmentationCode0
RelGAN: Relational Generative Adversarial Networks for Text GenerationCode0
Discovering Many Diverse Solutions with Bayesian OptimizationCode0
Mutual Information and Diverse Decoding Improve Neural Machine TranslationCode0
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component AnalysisCode0
FairER: Entity Resolution with Fairness ConstraintsCode0
Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual InformationCode0
The State and Fate of Linguistic Diversity and Inclusion in the NLP WorldCode0
Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape BlendingCode0
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in VideoCode0
Named Entity Recognition With Parallel Recurrent Neural NetworksCode0
Nash CoT: Multi-Path Inference with Preference EquilibriumCode0
Native Design Bias: Studying the Impact of English Nativeness on Language Model PerformanceCode0
Discord Questions: A Computational Approach To Diversity Analysis in News CoverageCode0
TripleE: Easy Domain Generalization via Episodic ReplayCode0
DISCERN: Diversity-based Selection of Centroids for k-Estimation and Rapid Non-stochastic ClusteringCode0
Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCoCode0
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