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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 2676–2700 of 9051 papers

TitleStatusHype
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment ApproachCode0
SEE-DPO: Self Entropy Enhanced Direct Preference Optimization—0
Diversity Helps Jailbreak Large Language Models—0
Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning—0
Hiring as Exploration—0
Growing a Tail: Increasing Output Diversity in Large Language Models—0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
Conditional Vendi Score: An Information-Theoretic Approach to Diversity Evaluation of Prompt-based Generative ModelsCode0
A Post-Training Enhanced Optimization Approach for Small Language Models—0
IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems—0
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities—0
Evaluating Creative Short Story Generation in Humans and Large Language ModelsCode0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Towards Leveraging News Media to Support Impact Assessment of AI Technologies—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
A Coverage-Guided Testing Framework for Quantum Neural Networks—0
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions—0
The Role of Domain Randomization in Training Diffusion Policies for Whole-Body Humanoid Control—0
Diversidade linguística e inclusão digital: desafios para uma ia brasileira—0
PRIMO: Progressive Induction for Multi-hop Open Rule Generation—0
Designing a Robust Radiology Report Generation System—0
Axiomatic Explainer Globalness via Optimal Transport—0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
α-TCVAE: On the relationship between Disentanglement and Diversity—0
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