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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 3151–3160 of 9051 papers

TitleStatusHype
Daleel: Simplifying Cloud Instance Selection Using Machine Learning—0
DAL: Dual Adversarial Learning for Dialogue Generation—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
DAFA: Diversity-Aware Feature Aggregation for Attention-Based Video Object Detection—0
DAEBAK!: Peripheral Diversity for Multilingual Word Sense Disambiguation—0
Automated Backend-Aware Post-Training Quantization—0
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation—0
Automated Adversarial Discovery for Safety Classifiers—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection—0
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