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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 401–425 of 9051 papers

TitleStatusHype
Diversity-Fair Online Selection—0
Parameterized Synthetic Text Generation with SimpleStoriesCode1
Hierarchical protein backbone generation with latent and structure diffusion—0
How Good Are Large Language Models for Course Recommendation in MOOCs?—0
Hypergraph Vision Transformers: Images are More than Nodes, More than Edges—0
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review—0
ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generation—0
ChatGPT as Linguistic Equalizer? Quantifying LLM-Driven Lexical Shifts in Academic Writing—0
Vector Quantized-Elites: Unsupervised and Problem-Agnostic Quality-Diversity Optimization—0
Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild—0
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows—0
Gradient-based Sample Selection for Faster Bayesian Optimization—0
PinRec: Outcome-Conditioned, Multi-Token Generative Retrieval for Industry-Scale Recommendation Systems—0
More diverse more adaptive: Comprehensive Multi-task Learning for Improved LLM Domain Adaptation in E-commerce—0
MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection—0
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
On the Dynamics of Mating Preferences in Genetic Programming—0
CamContextI2V: Context-aware Controllable Video GenerationCode1
When Less Is More: A Sparse Facial Motion Structure For Listening Motion Learning—0
KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection—0
NoveltyBench: Evaluating Language Models for Humanlike Diversity—0
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems—0
CREA: A Collaborative Multi-Agent Framework for Creative Content Generation with Diffusion Models—0
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