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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 491500 of 9051 papers

TitleStatusHype
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
De novo Drug Design using Reinforcement Learning with Multiple GPT AgentsCode1
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
Distributed speech separation in spatially unconstrained microphone arraysCode1
Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and DetectionCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
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