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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 7751–7775 of 9051 papers

TitleStatusHype
Large Scale Multi-Lingual Multi-Modal Summarization DatasetCode0
Better RAG using Relevant Information GainCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
PersoBench: Benchmarking Personalized Response Generation in Large Language ModelsCode0
CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data SynthesisCode0
Improving Contextualized Topic Models with Negative SamplingCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Self-Evolved Dynamic Expansion Model for Task-Free Continual LearningCode0
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and DiversityCode0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
CUCL: Codebook for Unsupervised Continual LearningCode0
Improved Generation of Synthetic Imaging Data Using Feature-Aligned DiffusionCode0
Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion LexiconCode0
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical DataCode0
Sparse Personalized Federated LearningCode0
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language ModelsCode0
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous VariableCode0
Improved Generalization of Weight Space Networks via AugmentationsCode0
Latent Space is Feature Space: Regularization Term for GANs Training on Limited DatasetCode0
Latent Variable Dialogue Models and their DiversityCode0
Efficient Quality-Diversity Optimization through Diverse Quality SpeciesCode0
What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?Code0
Personalized LLM for Generating Customized Responses to the Same Query from Different UsersCode0
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