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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 776–800 of 9051 papers

TitleStatusHype
Human-Aligned Skill Discovery: Balancing Behaviour Exploration and Alignment—0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
General Scene Adaptation for Vision-and-Language NavigationCode2
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution SamplesCode0
Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions—0
Making Sense of Data in the Wild: Data Analysis Automation at Scale—0
Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments—0
Is It Navajo? Accurate Language Detection in Endangered Athabaskan LanguagesCode0
Enhancing Synthetic Oversampling for Imbalanced Datasets Using Proxima-Orion Neighbors and q-Gaussian Weighting Technique—0
IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding—0
BAG: Body-Aligned 3D Wearable Asset Generation—0
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree searchCode0
Economic Implications of Corporate Governance and Corporate Social Responsibility: Evidence from Banks in Bangladesh—0
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment—0
CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image SegmentationCode0
Visual Generation Without GuidanceCode2
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
Search results diversification in competitive search—0
Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent—0
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic ExpressionsCode0
Evaluating and Improving Graph to Text Generation with Large Language ModelsCode0
Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks—0
Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images—0
Feature-based Evolutionary Diversity Optimization of Discriminating Instances for Chance-constrained Optimization Problems—0
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