SOTAVerified

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 42514260 of 9051 papers

TitleStatusHype
Siamese Graph Learning for Semi-supervised Age EstimationCode0
Architext: Language-Driven Generative Architecture Design0
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos0
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image0
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic NetworksCode0
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning0
Knowledge-augmented Few-shot Visual Relation Detection0
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous DrivingCode1
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