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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 5876–5900 of 9051 papers

TitleStatusHype
Towards social pattern characterization in egocentric photo-streams—0
Towards Standard Criteria for human evaluation of Chatbots: A Survey—0
Towards Summarizing Healthcare Questions in Low-Resource Setting—0
Toward Stable World Models: Measuring and Addressing World Instability in Generative Environments—0
Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation—0
Towards the Synthesis of Non-speech Vocalizations—0
Towards transparency in NLP shared tasks—0
Towards Universal Segmentations: UniSegments 1.0—0
Toward the Next Generation of News Recommender Systems—0
Toward Understanding the Impact of Staleness in Distributed Machine Learning—0
Toward Wireless Localization Using Multiple Reconfigurable Intelligent Surfaces—0
TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters—0
TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques—0
Tracking, exploring and analyzing recent developments in German-language online press in the face of the coronavirus crisis: cOWIDplus Analysis and cOWIDplus Viewer—0
Tracking of plus-ends reveals microtubule functional diversity in different cell types—0
Tracking Sports Players with Context-Conditioned Motion Models—0
Tractable Diversity: Scalable Multiperspective Ontology Management via Standpoint EL—0
Tradeoffs in Data Augmentation: An Empirical Study—0
Trading Off Diversity and Quality in Natural Language Generation—0
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale—0
A systematic approach to random data augmentation on graph neural networks—0
Training and Inference Methods for High-Coverage Neural Machine Translation—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing—0
Training Group Orthogonal Neural Networks with Privileged Information—0
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