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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 2026–2050 of 9051 papers

TitleStatusHype
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Evolutionary Policy Optimization—0
Visual Variational Autoencoder Prompt Tuning—0
good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval—0
Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation—0
TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning—0
Preference-Guided Diffusion for Multi-Objective Offline Optimization—0
ARFlow: Human Action-Reaction Flow Matching with Physical Guidance—0
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology DiagnosisCode0
When Debate Fails: Bias Reinforcement in Large Language Models—0
Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn DynamicsCode0
Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data—0
LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images—0
CAARMA: Class Augmentation with Adversarial Mixup Regularization—0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection—0
SynCity: Training-Free Generation of 3D Worlds—0
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos—0
Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMs—0
Autonomous AI imitators increase diversity in homogeneous information ecosystems—0
Temporal Regularization Makes Your Video Generator Stronger—0
Understanding the Generalization of In-Context Learning in Transformers: An Empirical StudyCode0
Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning—0
DivCon-NeRF: Generating Augmented Rays with Diversity and Consistency for Few-shot View Synthesis—0
Concept-as-Tree: Synthetic Data is All You Need for VLM PersonalizationCode0
KVShare: An LLM Service System with Efficient and Effective Multi-Tenant KV Cache Reuse—0
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