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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 6601–6625 of 9051 papers

TitleStatusHype
Learning to Generate Videos Using Neural Uncertainty Priors—0
Monotonic Robust Policy Optimization with Model Discrepancy—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY—0
Generalized Operating Procedure for Deep Learning: an Unconstrained Optimal Design Perspective—0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
The Pile: An 800GB Dataset of Diverse Text for Language ModelingCode2
Enhancing Pre-trained Language Model with Lexical Simplification—0
Artificial Intelligence Development Races in Heterogeneous Settings—0
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data—0
ARBERT & MARBERT: Deep Bidirectional Transformers for ArabicCode1
Evaluation and Comparison of Edge-Preserving Filters—0
A Hybrid Bandit Framework for Diversified Recommendation—0
Reconfigurable Intelligent Surface-assisted Networks: Phase Alignment Categories—0
Evolving the Behavior of Machines: From Micro to Macroevolution—0
An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge—0
Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers—0
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing ProblemsCode1
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
Recommenders with a mission: assessing diversity in newsrecommendations—0
STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Minimax Active Learning—0
ErGAN: Generative Adversarial Networks for Entity Resolution—0
Regularized Attentive Capsule Network for Overlapped Relation Extraction—0
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