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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 576–600 of 9051 papers

TitleStatusHype
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic DataCode1
Multi-Task Reinforcement Learning Enables Parameter Scaling—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence—0
Accelerating Earth Science Discovery via Multi-Agent LLM Systems—0
Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search—0
ProtComposer: Compositional Protein Structure Generation with 3D EllipsoidsCode2
CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation—0
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems—0
Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI SystemsCode0
Enhancing Collective Intelligence in Large Language Models Through Emotional Integration—0
Probabilistic Insights for Efficient Exploration Strategies in Reinforcement Learning—0
PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary AlgorithmsCode0
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models—0
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence—0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
ExpertGenQA: Open-ended QA generation in Specialized Domains—0
Controllable Motion Generation via Diffusion Modal CouplingCode0
Hypergraph Foundation Model—0
SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity—0
Diversity Covariance-Aware Prompt Learning for Vision-Language Models—0
HI-Series Algorithms A Hybrid of Substance Diffusion Algorithm and Collaborative Filtering—0
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling—0
Group Relative Policy Optimization for Image CaptioningCode0
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