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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 2451–2475 of 9051 papers

TitleStatusHype
Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches—0
A Survey on Self-Evolution of Large Language Models—0
UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation—0
Collaborative Perception Datasets in Autonomous Driving: A Survey—0
Elucidating the Design Space of Dataset CondensationCode1
Semantic-Rearrangement-Based Multi-Level Alignment for Domain Generalized Segmentation—0
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding—0
FineRec:Exploring Fine-grained Sequential RecommendationCode1
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning—0
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video CommentingCode0
Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation—0
DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving—0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
Global Counterfactual DirectionsCode0
Understanding the genetic basis of variation in meiotic recombination: past, present, and future—0
Evaluating AI for Law: Bridging the Gap with Open-Source Solutions—0
How Population Diversity Influences the Efficiency of Crossover—0
Enhance Robustness of Language Models Against Variation Attack through Graph Integration—0
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity—0
Tailoring Generative Adversarial Networks for Smooth Airfoil Design—0
ProTA: Probabilistic Token Aggregation for Text-Video Retrieval—0
Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction—0
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye trackingCode1
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