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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
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
Test-time Adaptation for Cross-modal Retrieval with Query Shift—0
ComPO: Community Preferences for Language Model Personalization—0
Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery—0
A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach—0
Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive VehiclesCode0
Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Game—0
Tighter Performance Theory of FedExProx—0
Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method—0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—0
GDPO: Learning to Directly Align Language Models with Diversity Using GFlowNets—0
On the Diversity of Synthetic Data and its Impact on Training Large Language Models—0
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling—0
Distribution-Aware Compensation Design for Sustainable Data Rights in Machine Learning—0
Theoretical Aspects of Bias and Diversity in Minimum Bayes Risk DecodingCode0
An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-MakingCode0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code GenerationCode0
SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection—0
Soft-Label Integration for Robust Toxicity ClassificationCode0
Compression using Discrete Multi-Level Divisor Transform for Heterogeneous Sensor Data—0
Measuring Diversity: Axioms and Challenges—0
MetaAlign: Align Large Language Models with Diverse Preferences during Inference TimeCode0
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