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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 22812290 of 9051 papers

TitleStatusHype
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree searchCode0
Anomaly Detection With Multiple-Hypotheses PredictionsCode0
Global Counterfactual DirectionsCode0
A Comparative Study of Question Answering over Knowledge BasesCode0
GFlowNets and variational inferenceCode0
Anomaly Detection in Video Sequence with Appearance-Motion CorrespondenceCode0
Semi-Discriminative Representation Loss for Online Continual LearningCode0
Anomaly-aware multiple instance learning for rare anemia disorder classificationCode0
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCode0
GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANsCode0
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