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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 5776–5800 of 9051 papers

TitleStatusHype
Advances in Multi-turn Dialogue Comprehension: A Survey—0
Recoverability of Ancestral Recombination Graph Topologies—0
Surrogate-Assisted Reference Vector Adaptation to Various Pareto Front Shapes for Many-Objective Bayesian OptimizationCode0
Game Theory for Adversarial Attacks and DefensesCode0
Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech EnhancementCode0
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity—0
Graph Meta Network for Multi-Behavior RecommendationCode1
Active learning for interactive satellite image change detection—0
TRUNet: Transformer-Recurrent-U Network for Multi-channel Reverberant Sound Source Separation—0
Evolutionary Computation-Assisted Brainwriting for Large-Scale Online Ideation—0
Enhanced Memory Network: The novel network structure for Symbolic Music GenerationCode0
A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction—0
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling—0
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
Heritable Nongenetic Information That is Independent of DNA and That Governs Organismal Development, Tissue Regeneration, and Tumor Architecture—0
InfiniteForm: A synthetic, minimal bias dataset for fitness applications—0
Max and Coincidence Neurons in Neural Networks—0
Boost Neural Networks by Checkpoints—0
Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages—0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
Decoupling Pragmatics: Discriminative Decoding for Referring Expression Generation—0
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation—0
Fake It Till You Make It: Face analysis in the wild using synthetic data alone—0
Robust Allocations with Diversity Constraints—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
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