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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 8776–8800 of 9051 papers

TitleStatusHype
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-NetworkCode0
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised LearningCode0
Differentially Private Learning Needs Better Model Initialization and Self-DistillationCode0
Exploring Sparsity for Parameter Efficient Fine Tuning Using WaveletsCode0
Exploring Precision and Recall to assess the quality and diversity of LLMsCode0
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
NNOSE: Nearest Neighbor Occupational Skill ExtractionCode0
Studying Cultural Differences in Emoji Usage across the East and the WestCode0
No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 LanguagesCode0
Rethinking and Refining the Distinct MetricCode0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
Differentiable Instruction Optimization for Cross-Task GeneralizationCode0
This part looks alike this: identifying important parts of explained instances and prototypesCode0
Exploring Model Consensus to Generate Translation ParaphrasesCode0
Rethinking Diversified and Discriminative Proposal Generation for Visual GroundingCode0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image ModelsCode0
DIDI: Diffusion-Guided Diversity for Offline Behavioral GenerationCode0
Non-blind optical degradation correction via frequency self-adaptive and finetune tacticsCode0
Diversity inducing Information Bottleneck in Model EnsemblesCode0
Dialogue Quality and Emotion Annotations for Customer Support ConversationsCode0
Nondeterminism and Instability in Neural Network OptimizationCode0
DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party DialogueCode0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
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