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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 3951–3975 of 9051 papers

TitleStatusHype
Diverse Large-Scale ITS Dataset Created from Continuous Learning for Real-Time Vehicle Detection—0
CSECU-DSG at SemEval-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based Approach—0
Generating Diverse and Competitive Play-Styles for Strategy Games—0
Generating Diverse and Descriptive Image Captions Using Visual Paraphrases—0
Generating Diverse and High-Quality Abstractive Summaries with Variational Transformers—0
cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model—0
PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs—0
Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm—0
An Experimental Prototype for Multistatic Asynchronous ISAC—0
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas—0
Generating Diverse Hypotheses for Inductive Reasoning—0
Generating Diverse Indoor Furniture Arrangements—0
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases—0
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning—0
Generating Diverse Realistic Laughter for Interactive Art—0
Generating Diverse Story Continuations with Controllable Semantics—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
Generating Diverse Translation by Manipulating Multi-Head Attention—0
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation—0
Generating Diverse Translations with Sentence Codes—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection—0
Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection—0
Generating Geological Facies Models with Fidelity to Diversity and Statistics of Training Images using Improved Generative Adversarial Networks—0
Diverse Image Style Transfer via Invertible Cross-Space Mapping—0
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