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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

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Showing 2291–2300 of 9051 papers

TitleStatusHype
Annotator-Centric Active Learning for Subjective NLP TasksCode0
GFlowNets and variational inferenceCode0
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
GenZSL: Generative Zero-Shot Learning Via Inductive Variational AutoencoderCode0
Generative Monoculture in Large Language ModelsCode0
Annotating and Characterizing Clinical Sentences with Explicit Why-QA CuesCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
High-dimensional Assisted Generative Model for Color Image RestorationCode0
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine TranslationCode0
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