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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 8526–8550 of 9051 papers

TitleStatusHype
A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic MeasuresCode0
Cross-model Back-translated Distillation for Unsupervised Machine TranslationCode0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCoCode0
Theoretical tool bridging cell polarities with development of robust morphologiesCode0
Another Diversity-Promoting Objective Function for Neural Dialogue GenerationCode0
Multi-Agent Reinforcement Learning with Focal Diversity OptimizationCode0
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in TextCode0
DIVE: Diversified Iterative Self-ImprovementCode0
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement LearningCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-IdentificationCode0
A Workbench for Autograding Retrieve/Generate SystemsCode0
Multidataset Independent Subspace Analysis with Application to Multimodal FusionCode0
Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric LearningCode0
Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and ChallengesCode0
Unsupervised Diverse Colorization via Generative Adversarial NetworksCode0
Anomaly Detection With Multiple-Hypotheses PredictionsCode0
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric MaterialsCode0
Closed-Form Information Capacity of Canonical Signaling ModelsCode0
Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emittersCode0
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScoreCode0
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation LearningCode0
SRN: Side-output Residual Network for Object Symmetry Detection in the WildCode0
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