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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 3401–3425 of 9051 papers

TitleStatusHype
Decomposed evaluations of geographic disparities in text-to-image models—0
1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis—0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
GANmut: Generating and Modifying Facial Expressions—0
Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning—0
Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation—0
Linear Contextual Bandits with Hybrid Payoff: RevisitedCode0
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
Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective—0
Contextual Distillation Model for Diversified Recommendation—0
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning—0
Pareto Front-Diverse Batch Multi-Objective Bayesian OptimizationCode0
FairCoT: Enhancing Fairness in Diffusion Models via Chain of Thought Reasoning of Multimodal Language Models—0
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning—0
Meta-Learning an Evolvable Developmental EncodingCode0
SimGen: Simulator-conditioned Driving Scene Generation—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
FouRA: Fourier Low Rank Adaptation—0
Decoding the Diversity: A Review of the Indic AI Research Landscape—0
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation—0
Data Augmentation by Fuzzing for Neural Test Generation—0
Unraveling Code-Mixing Patterns in Migration Discourse: Automated Detection and Analysis of Online Conversations on RedditCode0
Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions—0
Should XAI Nudge Human Decisions with Explanation Biasing?—0
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