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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 5851–5875 of 9051 papers

TitleStatusHype
Generalization in Mean Field Games by Learning Master Policies—0
Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots—0
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks—0
SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Prediction—0
Learning to Regrasp by Learning to PlaceCode1
Modeling Multi-granularity Segmentation for Rare Words in Neural Machine Translation—0
Improving Personalized Dialogue Generation Models with Data-level Distillation and Diversification—0
Analyzing the Habitable Zones of Circumbinary Planets Using Machine LearningCode0
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires—0
Simple Post-Training Robustness Using Test Time Augmentations and Random ForestCode0
DDS: A new device-degraded speech dataset for speech enhancement—0
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictionsCode1
Squash root microbiome transplants and metagenomic inspection for in situ arid adaptations—0
Towards Document-Level Paraphrase Generation with Sentence Rewriting and ReorderingCode1
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion—0
QoS-aware Big Service Composition using Distributed Co-Evolutionary AlgorithmCode0
Few-shot Quality-Diversity OptimizationCode0
Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward—0
A Temporal Variational Model for Story GenerationCode1
How to address cellular heterogeneity by distribution biology—0
BERT for Target Apps Selection: Analyzing the Diversity and Performance of BERT in Unified Mobile Search—0
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus ExplorationCode1
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained ModelsCode1
Error performance analysis of different modulations over TWDP fading channel—0
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
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