SOTAVerified

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 7701–7725 of 9051 papers

TitleStatusHype
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings—0
Video Storytelling: Textual Summaries for Events—0
360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers—0
View Invariant 3D Human Pose Estimation—0
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning—0
SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation—0
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation—0
SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model—0
SSNCSE_NLP@LT-EDI-ACL2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers—0
SSR: Speculative Parallel Scaling Reasoning in Test-time—0
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation—0
S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks—0
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent—0
StableFDG: Style and Attention Based Learning for Federated Domain Generalization—0
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning—0
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components—0
AGAD: Adversarial Generative Anomaly Detection—0
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes—0
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data—0
A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?—0
Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology—0
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms—0
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation—0
STAR-RISs: Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces—0
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