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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 10011010 of 9051 papers

TitleStatusHype
Few-Shot Video Object DetectionCode1
Noise Conditional Flow Model for Learning the Super-Resolution SpaceCode1
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
Forecasting Future World Events with Neural NetworksCode1
General Virtual Sketching Framework for Vector Line ArtCode1
AlphaFold Distillation for Protein DesignCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
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