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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 8601–8625 of 9051 papers

TitleStatusHype
Start Small: Training Controllable Game Level Generators without Training Data by Learning at Multiple SizesCode0
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?Code0
Reducing Overlearning through Disentangled Representations by Suppressing Unknown TasksCode0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement LearningCode0
Fast Texture Synthesis via Pseudo OptimizerCode0
FASTdoop: a versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applicationsCode0
Transformers as Neural Augmentors: Class Conditional Sentence Generation via Variational BayesCode0
Cheetah: Natural Language Generation for 517 African LanguagesCode0
Multi-Objective Quality Diversity OptimizationCode0
Multi-Objective Quality-Diversity for Crystal Structure PredictionCode0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
Multi-Objective Recommendation via Multivariate Policy LearningCode0
Diversity-Driven View Subset Selection for Indoor Novel View SynthesisCode0
A Diversity-Promoting Objective Function for Neural Conversation ModelsCode0
Anomaly Detection in Video Sequence with Appearance-Motion CorrespondenceCode0
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image ClassificationCode0
Multi-Perspective Stance DetectionCode0
Distinguishing Cell Phenotype Using Cell EpigenotypeCode0
Statistical Multicriteria Evaluation of LLM-Generated TextCode0
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal ExplorationCode0
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related TasksCode0
Anomaly-aware multiple instance learning for rare anemia disorder classificationCode0
A Hybrid Retrieval-Generation Neural Conversation ModelCode0
Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender SystemsCode0
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