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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 5976–6000 of 9051 papers

TitleStatusHype
Sketch Your Own GANCode1
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-IDCode1
Alleviating Mode Collapse in GAN via Diversity Penalty Module—0
Cycle-Consistent Inverse GAN for Text-to-Image Synthesis—0
Spike timing regularity in vestibular afferent neurons: How ionic currents influence sensory encoding mechanisms—0
Local Diversity and Ultra-Reliable Antenna Arrays—0
DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection—0
mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation—0
System Description for the CommonGen task with the POINTER model—0
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages—0
Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content—0
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse ParaphrasingCode1
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER—0
The Classical Language Toolkit: An NLP Framework for Pre-Modern LanguagesCode1
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
ARBERT \& MARBERT: Deep Bidirectional Transformers for ArabicCode0
Out-of-Core Surface Reconstruction via Global TGV MinimizationCode1
Batch Active Learning at ScaleCode0
Addressing materials' microstructure diversity using transfer learning—0
Personalized Trajectory Prediction via Distribution DiscriminationCode1
Why You Should Try the Real Data for the Scene Text Recognition—0
Estimation of functional diversity and species traits from ecological monitoring data—0
Reenvisioning Collaborative Filtering vs Matrix FactorizationCode1
The loss landscape of deep linear neural networks: a second-order analysis—0
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