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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 2276–2300 of 9051 papers

TitleStatusHype
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and GeneralizationCode0
MallowsPO: Fine-Tune Your LLM with Preference Dispersions—0
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings—0
Learning to Transform Dynamically for Better Adversarial TransferabilityCode1
DIDI: Diffusion-Guided Diversity for Offline Behavioral GenerationCode0
Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis—0
Lessons to learn for better safeguarding of genetic resources during tree pandemics: the case of ash dieback in Europe—0
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction TuningCode1
Towards Exploratory Quality Diversity Landscape Analysis—0
Naturally Private Recommendations with Determinantal Point Processes—0
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis—0
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning—0
Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic ScenariosCode0
No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models—0
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
A Workbench for Autograding Retrieve/Generate SystemsCode0
Multiple Realizability and the Rise of Deep Learning—0
Goals as Reward-Producing ProgramsCode1
Orthogonally Initiated Particle Swarm Optimization with Advanced Mutation for Real-Parameter Optimization—0
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in TextCode0
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine TranslationCode1
Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking—0
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