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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 681690 of 9051 papers

TitleStatusHype
Distributed speech separation in spatially unconstrained microphone arraysCode1
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
DivClust: Controlling Diversity in Deep ClusteringCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline GenerationCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
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