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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 7476–7500 of 9051 papers

TitleStatusHype
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image ClassificationCode0
An Abstraction Model for Semantic Segmentation Algorithms—0
Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization—0
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity—0
A Regression Framework for Predicting User's Next Location using Call Detail Records—0
Reconfigurable Intelligent Surface Aided NOMA Networks—0
Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity—0
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and LimitationsCode0
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension—0
SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing ImagesCode0
Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data—0
Contextually Plausible and Diverse 3D Human Motion Prediction—0
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversionCode0
Direction Concentration Learning: Enhancing Congruency in Machine LearningCode0
Enhanced Spatially Interleaved Techniques for Multi-View Distributed Video Coding—0
Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level—0
C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds—0
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic ManuscriptsCode0
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework—0
Non-linearity identification for construction workers' personality-safety behaviour predictive relationship using neural network and linear regression modelling—0
Unsupervised Neural Dialect Translation with Commonality and Diversity ModelingCode0
Is Feature Diversity Necessary in Neural Network Initialization?Code0
Collecting Entailment Data for Pretraining: New Protocols and Negative Results—0
Neural Module Networks for Reasoning over TextCode0
Scalability in Perception for Autonomous Driving: Waymo Open DatasetCode2
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