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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 15911600 of 9051 papers

TitleStatusHype
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference0
Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data0
Automated Backend-Aware Post-Training Quantization0
Automated Adversarial Discovery for Safety Classifiers0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction0
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