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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 2701–2725 of 9051 papers

TitleStatusHype
Call for Action: towards the next generation of symbolic regression benchmark—0
An Online Approach to Dynamic Channel Access and Transmission Scheduling—0
AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis—0
Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery—0
Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs—0
CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring—0
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling—0
AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization—0
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings—0
CAD: Photorealistic 3D Generation via Adversarial Distillation—0
Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement—0
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process—0
CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems—0
CAARMA: Class Augmentation with Adversarial Mixup Regularization—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection—0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
Diversity driven Query Rewriting in Search Advertising—0
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
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