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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 4401–4425 of 9051 papers

TitleStatusHype
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge—0
Benchmarking Monocular 3D Dog Pose Estimation Using In-The-Wild Motion Capture Data—0
Inference Latency Prediction at the Edge—0
DH-Set: Improving Vision-Language Alignment with Diverse and Hybrid Set-Embeddings Learning—0
Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction—0
An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation—0
Inferring M-Best Diverse Labelings in a Single One—0
An Analysis of Phenotypic Diversity in Multi-Solution Optimization—0
Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems—0
Infant Cry Classification with Graph Convolutional Networks—0
Inferring Missing Categorical Information in Noisy and Sparse Web Markup—0
Infinite forecast combinations based on Dirichlet process—0
Information entropy as an anthropomorphic concept—0
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception—0
Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction—0
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning—0
An Analysis of Generative Methods for Multiple Image Inpainting—0
2-D Coherence Factor for Sidelobe and Ghost Suppressions in Radar Imaging—0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
Benchmarking General-Purpose In-Context Learning—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification—0
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology—0
IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource—0
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