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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 2101–2125 of 9051 papers

TitleStatusHype
Concept-skill Transferability-based Data Selection for Large Vision-Language ModelsCode1
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
STAR: Scale-wise Text-to-image generation via Auto-Regressive representationsCode2
Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation—0
Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning—0
Consistency-diversity-realism Pareto fronts of conditional image generative modelsCode2
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks—0
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian LanguagesCode2
Linear Contextual Bandits with Hybrid Payoff: RevisitedCode0
Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
Contextual Distillation Model for Diversified Recommendation—0
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning—0
Meta-Learning an Evolvable Developmental EncodingCode0
Decoding the Diversity: A Review of the Indic AI Research Landscape—0
Optimal Kernel Orchestration for Tensor Programs with KorchCode1
SimGen: Simulator-conditioned Driving Scene Generation—0
A Large-scale Universal Evaluation Benchmark For Face Forgery DetectionCode1
FouRA: Fourier Low Rank Adaptation—0
MMRel: A Relation Understanding Benchmark in the MLLM EraCode1
Pareto Front-Diverse Batch Multi-Objective Bayesian OptimizationCode0
Depth Anything V2Code9
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