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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 3626–3650 of 9051 papers

TitleStatusHype
Evaluation of Faithfulness Using the Longest Supported Subsequence—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Diverse Policies Converge in Reward-free Markov Decision ProcesseCode0
MolGrapher: Graph-based Visual Recognition of Chemical StructuresCode1
The YOLO model that still excels in document layout analysis—0
Generalising sequence models for epigenome predictions with tissue and assay embeddings—0
Diversity Measures: Domain-Independent Proxies for Failure in Language Model QueriesCode0
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User SimulatorCode1
Comparing Measures of Linguistic Diversity Across Social Media Language Data and Census Data at Subnational Geographic Areas—0
DynED: Dynamic Ensemble Diversification in Data Stream ClassificationCode0
DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant RecommendationsCode0
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification—0
Self-Feedback DETR for Temporal Action Detection—0
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations—0
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
Adaptive pruning-based Newton's method for distributed learning—0
UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment—0
TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric PerspectiveCode1
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation—0
DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization—0
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed RecognitionCode1
UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning—0
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