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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 7376–7400 of 9051 papers

TitleStatusHype
Self-Feedback DETR for Temporal Action Detection—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
AlphaStar: An Evolutionary Computation Perspective—0
Self-Paced Learning with Diversity—0
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images—0
A Low Complexity Space-Frequency Multiuser Scheduling Algorithm—0
Self-Referential Quality Diversity Through Differential Map-Elites—0
A local continuum model of cell-cell adhesion—0
Self-reinforcing Unsupervised Matching—0
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering—0
A Load Balanced Recommendation Approach—0
All You Need Is Sex for Diversity—0
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography—0
Allowing for equal opportunities for artists in music recommendation—0
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks—0
Self-supervised Human Mesh Recovery with Cross-Representation Alignment—0
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation—0
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling—0
Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model—0
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images—0
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz—0
Self-supervised Representation Learning for Trip Recommendation—0
Wisdom of Crowds cluster ensemble—0
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives—0
Self-Supervised Visual Acoustic Matching—0
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