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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 6126–6150 of 9051 papers

TitleStatusHype
Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network—0
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure—0
Online Knowledge Distillation via Multi-branch Diversity Enhancement—0
Online News Media Website Ranking Using User Generated Content—0
Machine Learning in Nuclear Physics—0
Which Channel in 6G, Low-rank or Full-rank, more needs RIS from a Perspective of DoF?—0
On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization—0
Affinity-based extension of non-extensive entropy and statistical mechanics—0
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY—0
On Nondeterminism and Instability in Optimizing Neural Networks—0
On Nondeterminism and Instability in Neural Network Optimization—0
On Paraphrase Identification Corpora—0
On Performance of Fluid Antenna System using Maximum Ratio Combining—0
Uncertainty-Driven Action Quality Assessment—0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection—0
ARTEMIS: Using GANs with Multiple Discriminators to Generate Art—0
On properties driving diversity index selection—0
Uncertainty Quantification for Atlas-Level Cell Type Transfer—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond—0
On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes—0
On Stein Variational Neural Network Ensembles—0
ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture—0
On the Amplification of Linguistic Bias through Unintentional Self-reinforcement Learning by Generative Language Models -- A Perspective—0
The quest for the definition of life—0
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