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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 4501–4550 of 9051 papers

TitleStatusHype
Improving Diversity and Reducing Redundancy in Paragraph Captions—0
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations—0
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis—0
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting—0
Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss—0
Improving Diversity of Neural Text Generation via Inverse Probability Weighting—0
BLIP: Facilitating the Exploration of Undesirable Consequences of Digital Technologies—0
Don't Change Me! User-Controllable Selective Paraphrase Generation—0
Top-Down Bayesian Posterior Sampling for Sum-Product Networks—0
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar—0
Blind Motion Deblurring through SinGAN Architecture—0
Improving Generalization of Hate Speech Detection Systems to Novel Target Groups via Domain Adaptation—0
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation—0
Improving Human Motion Prediction Through Continual Learning—0
Improving In-Context Few-Shot Learning via Self-Supervised Training—0
BLEU Neighbors: A Reference-less Approach to Automatic Evaluation—0
Improving Intelligence of Evolutionary Algorithms Using Experience Share and Replay—0
Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization—0
Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space—0
HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization—0
Weight Ensembling Improves Reasoning in Language Models—0
Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation—0
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder—0
Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations—0
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation—0
Blessing of Class Diversity in Pre-training—0
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets—0
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis—0
Improving negation detection with negation-focused pre-training—0
Improving negation detection with negation-focused pre-training—0
TopicDiff: A Topic-enriched Diffusion Approach for Multimodal Conversational Emotion Detection—0
Topic-focused Dynamic Information Filtering in Social Media—0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation—0
Topic Modeling as Multi-Objective Contrastive Optimization—0
Improving Neural Network Generalization via Promoting Within-Layer Diversity—0
Topic Modeling in Marathi—0
Improving NSGA-II with an Adaptive Mutation Operator—0
Black Feminist Musings on Algorithmic Oppression—0
Improving Out-of-Distribution Robustness of Classifiers Through Interpolated Generative Models—0
Improving Out-of-Distribution Robustness of Classifiers via Generative Interpolation—0
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning—0
Improving Personalized Dialogue Generation Models with Data-level Distillation and Diversification—0
Improving Personalized Explanation Generation through Visualization—0
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition—0
Improving Query Safety at Pinterest—0
Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts—0
Improving Recommendation System Serendipity Through Lexicase Selection—0
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