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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 4601–4625 of 9051 papers

TitleStatusHype
Incremental training of multi-generative adversarial networks—0
Total consensus under high reproductive-variance conditions—0
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning—0
Indian Masked Faces in the Wild Dataset—0
Total-Duration-Aware Duration Modeling for Text-to-Speech Systems—0
Indicator & crowding Distance-Based Evolutionary Algorithm for Combined Heat and Power Economic Emission Dispatch—0
To think inside the box, or to think out of the box? Scientific discovery via the reciprocation of insights and concepts—0
BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects—0
IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding—0
IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages—0
Touring sampling with pushforward maps—0
WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data—0
Individual-Level SNP Diversity and Similarity Profiles—0
Bilevel Scheduled Sampling for Dialogue Generation—0
IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages—0
IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource—0
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph—0
Infant Cry Classification with Graph Convolutional Networks—0
Inference Latency Prediction at the Edge—0
Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes—0
Inferring M-Best Diverse Labelings in a Single One—0
Inferring Missing Categorical Information in Noisy and Sparse Web Markup—0
Inferring Multi-Period Optimal Portfolios via Detrending Moving Average Cluster Entropy—0
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