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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

Showing 20762100 of 9051 papers

TitleStatusHype
ArchiGuesser -- AI Art Architecture Educational GameCode0
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI RecommendationCode0
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive RegularizationCode0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
How Well Do LLMs Identify Cultural Unity in Diversity?Code0
How well do you know your summarization datasets?Code0
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofingCode0
Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA ArchitecturesCode0
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe GenerationCode0
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCoCode0
ARBERT \& MARBERT: Deep Bidirectional Transformers for ArabicCode0
How to partition diversityCode0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric MaterialsCode0
Closed-Form Information Capacity of Canonical Signaling ModelsCode0
A Corpus-free State2Seq User Simulator for Task-oriented DialogueCode0
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?Code0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
A Corpus for Reasoning About Natural Language Grounded in PhotographsCode0
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