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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 1751–1775 of 9051 papers

TitleStatusHype
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
Information Density Principle for MLLM BenchmarksCode0
Conditional Diffusion Models with Classifier-Free Gibbs-like GuidanceCode0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
Concise and interpretable multi-label rule setsCode0
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
Increasing Entropy to Boost Policy Gradient Performance on Personalization TasksCode0
IndicEval-XL: Bridging Linguistic Diversity in Code Generation Across Indic LanguagesCode0
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender DiversityCode0
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point ProcessesCode0
Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain EnvironmentsCode0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region FittingCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
Concept-as-Tree: Synthetic Data is All You Need for VLM PersonalizationCode0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Computing recommendations via a Knowledge Graph-aware AutoencoderCode0
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