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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 1926–1950 of 9051 papers

TitleStatusHype
Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies—0
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research—0
Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models—0
MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation—0
SLURG: Investigating the Feasibility of Generating Synthetic Online Fallacious Discourse—0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning—0
How Do I Do That? Synthesizing 3D Hand Motion and Contacts for Everyday Interactions—0
Voice Conversion with Diverse Intonation using Conditional Variational Auto-Encoder—0
Evaluating the Diversity and Quality of LLM Generated Content—0
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach—0
Real-World Depth Recovery via Structure Uncertainty Modeling and Inaccurate GT Depth Fitting—0
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets—0
Unravelling Technical debt topics through Time, Programming Languages and Repository—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Multi-Agent Reinforcement Learning for Decentralized Reservoir Management via Murmuration Intelligence—0
X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents—0
Large Language Model-Informed Feature Discovery Improves Prediction and Interpretation of Credibility Perceptions of Visual Content—0
Using LLMs as prompt modifier to avoid biases in AI image generators—0
Weight Ensembling Improves Reasoning in Language Models—0
Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading—0
Diversity-Fair Online Selection—0
Relation-Rich Visual Document Generator for Visual Information ExtractionCode0
Can genomic analysis actually estimate past population size?—0
The Impact of Model Zoo Size and Composition on Weight Space LearningCode0
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