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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 8176–8200 of 9051 papers

TitleStatusHype
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
Bayesian identification of bacterial strains from sequencing dataCode0
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
MapGo: Model-Assisted Policy Optimization for Goal-Oriented TasksCode0
Batched Large-scale Bayesian Optimization in High-dimensional SpacesCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Diversity Networks: Neural Network Compression Using Determinantal Point ProcessesCode0
Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding BoundariesCode0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
Mapping the stereotyped behaviour of freely-moving fruit fliesCode0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural NetworksCode0
AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMsCode0
Computational detection of antigen specific B cell receptors following immunizationCode0
Skeleton-to-Response: Dialogue Generation Guided by Retrieval MemoryCode0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content CreationCode0
Global Counterfactual DirectionsCode0
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree searchCode0
GFlowNets and variational inferenceCode0
Diversity Measures: Domain-Independent Proxies for Failure in Language Model QueriesCode0
Batch Decorrelation for Active Metric LearningCode0
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-ExpertsCode0
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCode0
Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape AnalysisCode0
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