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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 1151–1175 of 9051 papers

TitleStatusHype
The chemical space of terpenes: insights from data science and AICode1
AugMax: Adversarial Composition of Random Augmentations for Robust TrainingCode1
Robustness of Graph Neural Networks at ScaleCode1
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face GenerationCode1
MUGL: Large Scale Multi Person Conditional Action Generation with LocomotionCode1
Generative Adversarial Graph Convolutional Networks for Human Action SynthesisCode1
A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images GenerationCode1
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic FacesCode1
Trigger Hunting with a Topological Prior for Trojan DetectionCode1
UniPELT: A Unified Framework for Parameter-Efficient Language Model TuningCode1
Considering user agreement in learning to predict the aesthetic qualityCode1
Graph Meta Network for Multi-Behavior RecommendationCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture GenerationCode1
Parallel Refinements for Lexically Constrained Text Generation with BARTCode1
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-IdentificationCode1
Learning to Regrasp by Learning to PlaceCode1
Towards Document-Level Paraphrase Generation with Sentence Rewriting and ReorderingCode1
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictionsCode1
A Temporal Variational Model for Story GenerationCode1
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus ExplorationCode1
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained ModelsCode1
Illuminating Diverse Neural Cellular Automata for Level GenerationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
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