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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 8301–8325 of 9051 papers

TitleStatusHype
Diverse Online Feature SelectionCode0
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks—0
Object detection and tracking benchmark in industry based on improved correlation filter—0
Dank Learning: Generating Memes Using Deep Neural NetworksCode0
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations—0
PatchFCN for Intracranial Hemorrhage Detection—0
A Simple Method for Commonsense ReasoningCode0
Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and BrowsingCode0
Variational Autoencoder with Arbitrary ConditioningCode0
Ring Migration Topology Helps Bypassing Local Optima—0
Recent advances and opportunities in scene classification of aerial images with deep models—0
Conservative Exploration using Interleaving—0
AID++: An Updated Version of AID on Scene Classification—0
An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints—0
A Common Framework for Interactive Texture Transfer—0
GroupCap: Group-Based Image Captioning With Structured Relevance and Diversity Constraints—0
Interactive Image Segmentation With Latent DiversityCode0
Oral-Motor and Lexical Diversity During Naturalistic Conversations in Adults with Autism Spectrum Disorder—0
Reddit: A Gold Mine for Personality Prediction—0
Quantitative Semantic Variation in the Contexts of Concrete and Abstract Words—0
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences—0
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics—0
The Externalities of Exploration and How Data Diversity Helps Exploitation—0
Generative Adversarial Networks for Unsupervised Object Co-localization—0
Video Description: A Survey of Methods, Datasets and Evaluation Metrics—0
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