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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 3676–3700 of 9051 papers

TitleStatusHype
Enhance Robustness of Language Models Against Variation Attack through Graph Integration—0
Understanding the genetic basis of variation in meiotic recombination: past, present, and future—0
Tailoring Generative Adversarial Networks for Smooth Airfoil Design—0
Global Counterfactual DirectionsCode0
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity—0
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
On the Scalability of GNNs for Molecular Graphs—0
Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image AnalysisCode0
The dynamics of diversity on corporate boards—0
IMIL: Interactive Medical Image Learning Framework—0
Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection—0
LMEraser: Large Model Unlearning through Adaptive Prompt TuningCode0
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem—0
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem—0
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language PromptingCode0
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition—0
Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks—0
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation—0
MAD Speech: Measures of Acoustic Diversity of Speech—0
Promoting the linguistic diversity of TEI in the Maghreb and the Arab region—0
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language ModelsCode0
DESTEIN: Navigating Detoxification of Language Models via Universal Steering Pairs and Head-wise Activation FusionCode0
Measuring Diversity of Game Scenarios—0
Taming Latent Diffusion Model for Neural Radiance Field Inpainting—0
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