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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 3826–3850 of 9051 papers

TitleStatusHype
From Matching with Diversity Constraints to Matching with Regional Quotas—0
GRADE: Quantifying Sample Diversity in Text-to-Image Models—0
From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models—0
From Posterior Sampling to Meaningful Diversity in Image Restoration—0
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning—0
Diverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models—0
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning—0
Diverse Neural Network Learns True Target Functions—0
From Shadow Segmentation to Shadow Removal—0
Boosting Dialog Response Generation—0
Covariance Matrix Adaptation MAP-Annealing—0
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology—0
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion—0
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control—0
Diverse mini-batch Active Learning—0
Full-band General Audio Synthesis with Score-based Diffusion—0
A Tutorial On Intersectionality in Fair Rankings—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Diverse Melody Generation from Chinese Lyrics via Mutual Information Maximization—0
CPLM, a Parallel Corpus for Mexican Languages: Development and Interface—0
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching—0
Diverse Machine Translation with a Single Multinomial Latent Variable—0
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