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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 14211430 of 9051 papers

TitleStatusHype
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
Practical Wide-Angle Portraits Correction with Deep Structured ModelsCode1
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-IDCode1
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree SearchCode1
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial SensorsCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Data Curation Alone Can Stabilize In-context LearningCode1
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
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