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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 1976–2000 of 9051 papers

TitleStatusHype
CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer Models—0
cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model—0
CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models—0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—0
CTR-Guided Generative Query Suggestion in Conversational Search—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
Condorcet's Jury Theorem for Consensus Clustering and its Implications for Diversity—0
Condition-Transforming Variational AutoEncoder for Conversation Response Generation—0
ASR4REAL: An extended benchmark for speech models—0
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation—0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
A unified framework based on graph consensus term for multi-view learning—0
Cuid: A new study of perceived image quality and its subjective assessment—0
Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions—0
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network—0
Designing a Robust Radiology Report Generation System—0
Cultivating DNN Diversity for Large Scale Video Labelling—0
Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation—0
Semantic and Expressive Variation in Image Captions Across Languages—0
A Spiking Neuron Synaptic Plasticity Model Optimized for Unsupervised Learning—0
Cultural Diversity and Its Impact on Governance—0
Cultural Evaluations of Vision-Language Models Have a Lot to Learn from Cultural Theory—0
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration—0
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