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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 2701–2750 of 9051 papers

TitleStatusHype
A Nonlocal InSAR Filter for High-Resolution DEM Generation from TanDEM-X Interferograms—0
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters—0
A Nonlinear African Vulture Optimization Algorithm Combining Henon Chaotic Mapping Theory and Reverse Learning Competition Strategy—0
Ego-Downward and Ambient Video based Person Location Association—0
Diversity Induced Environment Design via Self-Play—0
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
Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks—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
Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement—0
CAD: Photorealistic 3D Generation via Adversarial Distillation—0
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process—0
Efficient Skill Discovery via Regret-Aware Optimization—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems—0
CAARMA: Class Augmentation with Adversarial Mixup Regularization—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking—0
Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction—0
Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents—0
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity—0
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software—0
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection—0
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
Diversity Enhanced Table-to-Text Generation via Type Control—0
Diversity Enhancement for Micro-Differential Evolution—0
Diversity Enhancement via Magnitude—0
Diversity Enhances an LLM's Performance in RAG and Long-context Task—0
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation—0
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings—0
Diversity-Fair Online Selection—0
Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer—0
Diversity from Human Feedback—0
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection—0
Diversity driven Query Rewriting in Search Advertising—0
Efficient Scene Recovery Using Luminous Flux Prior—0
Diversity Handling In Evolutionary Landscape—0
Diversity Helps Jailbreak Large Language Models—0
Diversity improves performance in excitable networks—0
Diversity in Action: General-Sum Multi-Agent Continuous Inverse Optimal Control—0
Diversity in Choice as Majorization—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
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