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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 2776–2800 of 9051 papers

TitleStatusHype
On Languaging a Simulation Engine—0
Topic-to-essay generation with knowledge-based content selection—0
Finding Near-Optimal Portfolios With Quality-Diversity—0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities—0
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated DataCode1
CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models—0
Hands-Free VR—0
Fine-Grained Detoxification via Instance-Level Prefixes for Large Language ModelsCode0
Ten computational challenges in human virome studies—0
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control—0
DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization—0
Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns—0
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich ReasoningCode1
Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene UnderstandingCode3
Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting—0
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation SystemsCode1
Visual Hallucinations of Multi-modal Large Language ModelsCode1
Measuring Multimodal Mathematical Reasoning with MATH-Vision DatasetCode2
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization—0
LongWanjuan: Towards Systematic Measurement for Long Text QualityCode1
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness—0
Diversity-Aware k-Maximum Inner Product Search Revisited—0
Se^2: Sequential Example Selection for In-Context Learning—0
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual LearningCode1
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