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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 2276–2300 of 9051 papers

TitleStatusHype
Anomaly Detection With Multiple-Hypotheses PredictionsCode0
GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANsCode0
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the WildCode0
Gradient Estimators for Implicit ModelsCode0
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
A Comparative Study of Question Answering over Knowledge BasesCode0
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial NetworksCode0
Anomaly Detection in Video Sequence with Appearance-Motion CorrespondenceCode0
Global Counterfactual DirectionsCode0
Semi-Discriminative Representation Loss for Online Continual LearningCode0
Anomaly-aware multiple instance learning for rare anemia disorder classificationCode0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture ModelingCode0
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree searchCode0
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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