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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 1951–1975 of 9051 papers

TitleStatusHype
Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space—0
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation—0
Hierarchical protein backbone generation with latent and structure diffusion—0
ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generation—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
Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild—0
ChatGPT as Linguistic Equalizer? Quantifying LLM-Driven Lexical Shifts in Academic Writing—0
Gradient-based Sample Selection for Faster Bayesian Optimization—0
Vector Quantized-Elites: Unsupervised and Problem-Agnostic Quality-Diversity Optimization—0
DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows—0
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning—0
MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection—0
More diverse more adaptive: Comprehensive Multi-task Learning for Improved LLM Domain Adaptation in E-commerce—0
PinRec: Outcome-Conditioned, Multi-Token Generative Retrieval for Industry-Scale Recommendation Systems—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
On the Dynamics of Mating Preferences in Genetic Programming—0
KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection—0
When Less Is More: A Sparse Facial Motion Structure For Listening Motion Learning—0
CREA: A Collaborative Multi-Agent Framework for Creative Content Generation with Diffusion Models—0
Cooperation guides evolution in a minimal model of biological evolution—0
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems—0
NoveltyBench: Evaluating Language Models for Humanlike Diversity—0
A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality—0
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