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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 8876–8900 of 9051 papers

TitleStatusHype
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense ReasoningCode0
DeLiGAN : Generative Adversarial Networks for Diverse and Limited DataCode0
Submodular Batch Selection for Training Deep Neural NetworksCode0
Evolutionary bagging for ensemble learningCode0
Automatic Fused Multimodal Deep Learning for Plant IdentificationCode0
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data AugmentationCode0
TWOLAR: a TWO-step LLM-Augmented distillation method for passage RerankingCode0
Evidence for a multi-level trophic organization of the human gut microbiomeCode0
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music CommentCode0
One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversityCode0
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
An Empirical Study of Translation Hypothesis Ensembling with Large Language ModelsCode0
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic NetworksCode0
RK-core: An Established Methodology for Exploring the Hierarchical Structure within DatasetsCode0
Event Transition Planning for Open-ended Text GenerationCode0
Sub-SA: Strengthen In-context Learning via Submodular Selective AnnotationCode0
To Distill or Not to Distill? On the Robustness of Robust Knowledge DistillationCode0
EventDrop: data augmentation for event-based learningCode0
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
A Grid-Based Evolutionary Algorithm for Many-Objective OptimizationCode0
Evaluator for Emotionally Consistent ChatbotsCode0
"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with DefinitionCode0
RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual CompensationCode0
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Visual Information Guided Zero-Shot Paraphrase GenerationCode0
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