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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 2476–2500 of 9051 papers

TitleStatusHype
Evaluating AI for Law: Bridging the Gap with Open-Source Solutions—0
LMEraser: Large Model Unlearning through Adaptive Prompt TuningCode0
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem—0
IMIL: Interactive Medical Image Learning Framework—0
The dynamics of diversity on corporate boards—0
On the Scalability of GNNs for Molecular Graphs—0
Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image AnalysisCode0
VBR: A Vision Benchmark in RomeCode2
Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection—0
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem—0
Forcing Diffuse Distributions out of Language ModelsCode1
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language PromptingCode0
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation—0
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition—0
Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks—0
Promoting the linguistic diversity of TEI in the Maghreb and the Arab region—0
MAD Speech: Measures of Acoustic Diversity of Speech—0
DESTEIN: Navigating Detoxification of Language Models via Universal Steering Pairs and Head-wise Activation FusionCode0
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language ModelsCode0
Measuring Diversity of Game Scenarios—0
Taming Latent Diffusion Model for Neural Radiance Field Inpainting—0
Memory Sharing for Large Language Model based AgentsCode1
in2IN: Leveraging individual Information to Generate Human INteractionsCode2
Text-Driven Diverse Facial Texture Generation via Progressive Latent-Space Refinement—0
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