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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 1726–1750 of 9051 papers

TitleStatusHype
Input-gradient space particle inference for neural network ensemblesCode0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal StudyCode0
Active Learning for Regression Using Greedy SamplingCode0
Insect Identification in the Wild: The AMI DatasetCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain EnvironmentsCode0
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced ClassificationCode0
Information Density Principle for MLLM BenchmarksCode0
A Block-Based Adaptive Decoupling Framework for Graph Neural NetworksCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
A Hybrid Retrieval-Generation Neural Conversation ModelCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical StudyCode0
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with DroneCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point ProcessesCode0
Increasing Entropy to Boost Policy Gradient Performance on Personalization TasksCode0
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender DiversityCode0
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region FittingCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
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