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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

Showing 401410 of 9051 papers

TitleStatusHype
SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote SensingCode1
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-GuidanceCode1
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory PredictionCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism via Dual Diffusion Models and GPT PromptingCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic DataCode1
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