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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 8326–8350 of 9051 papers

TitleStatusHype
Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech—0
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Sparse Antenna and Pulse Placement for Colocated MIMO Radar—0
A Nonlocal InSAR Filter for High-Resolution DEM Generation from TanDEM-X Interferograms—0
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity—0
Accelerating CNN inference on FPGAs: A Survey—0
Omega: An Architecture for AI Unification—0
Multi-Task Determinantal Point Processes for Recommendation—0
Do Neural Network Cross-Modal Mappings Really Bridge Modalities?—0
Progressive Ensemble Networks for Zero-Shot Recognition—0
Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems—0
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography—0
Omega: An Architecture for AI Unification—0
Social diversity for reducing the impact of information cascades on social learning—0
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular MaximizationCode0
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Pool-Based Sequential Active Learning for RegressionCode0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Mean and Variance of Phylogenetic Trees—0
Ensemble Soft-Margin Softmax Loss for Image Classification—0
Text-mining and ontologies: new approaches to knowledge discovery of microbial diversity—0
Rethinking Diversified and Discriminative Proposal Generation for Visual GroundingCode0
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization—0
Comparing heterogeneous visual gestures for measuring the diversity of visual speech signals—0
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