SOTAVerified

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

Showing 16511675 of 9051 papers

TitleStatusHype
Intentional Computational Level DesignCode0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
Integrating Present and Past in Unsupervised Continual LearningCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
Interactive Image Segmentation With Latent DiversityCode0
Is ChatGPT A Good Keyphrase Generator? A Preliminary StudyCode0
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy LearningCode0
Instance-wise Supervision-level Optimization in Active LearningCode0
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case StudyCode0
Attribute Diversity Determines the Systematicity Gap in VQACode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
Attributed Graph Clustering via Adaptive Graph ConvolutionCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
Attribute-aware Diversification for Sequential RecommendationsCode0
Attribute Alignment: Controlling Text Generation from Pre-trained Language ModelsCode0
Attraction-Repulsion clustering with applications to fairnessCode0
Attesting Distributional Properties of Training Data for Machine LearningCode0
A Learned Representation For Artistic StyleCode0
Insect Identification in the Wild: The AMI DatasetCode0
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI ModelsCode0
Input-gradient space particle inference for neural network ensemblesCode0
Privacy-preserving datasets by capturing feature distributions with Conditional VAEsCode0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Albumentations: fast and flexible image augmentationsCode0
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