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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 4376–4400 of 9051 papers

TitleStatusHype
Diverse Priors for Deep Reinforcement Learning—0
Iteratively Learn Diverse Strategies with State Distance Information—0
MCC-KD: Multi-CoT Consistent Knowledge DistillationCode0
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation—0
Semantic and Expressive Variation in Image Captions Across Languages—0
Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems—0
Diversity Preferences, Affirmative Action and Choice Rules—0
Shortcuts for causal discovery of nonlinear models by score matching—0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Fundamental Limits of Membership Inference Attacks on Machine Learning Models—0
Controlled Randomness Improves the Performance of Transformer Models—0
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models—0
Time-Modulated Intelligent Reflecting Surface for Waveform Security—0
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
Benchmarking and Improving Text-to-SQL Generation under AmbiguityCode0
Knowledge Graph Context-Enhanced Diversified RecommendationCode0
PSGText: Stroke-Guided Scene Text Editing with PSP Module—0
CLAIR: Evaluating Image Captions with Large Language Models—0
Quality-Diversity through AI Feedback—0
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation—0
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification—0
Program Translation via Code Distillation—0
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control—0
An Empirical Study of Translation Hypothesis Ensembling with Large Language ModelsCode0
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