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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 12811290 of 9051 papers

TitleStatusHype
AutoMix: Automatically Mixing Language ModelsCode1
Automating Rigid Origami DesignCode1
Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture GenerationCode1
Graph Neural PDE Solvers with Conservation and Similarity-EquivarianceCode1
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
Mask as Supervision: Leveraging Unified Mask Information for Unsupervised 3D Pose EstimationCode1
Diverse Topology Optimization using Modulated Neural FieldsCode1
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