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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 7701–7725 of 9051 papers

TitleStatusHype
Energy Clustering for Unsupervised Person Re-identification—0
Heterogeneous Collaborative Filtering—0
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
The economics of minority language use: theory and empirical evidence for a language game model—0
BooVAE: Boosting Approach for Continual Learning of VAECode0
Adaptively Sparse TransformersCode1
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in SummarizationCode0
Latent Part-of-Speech Sequences for Neural Machine Translation—0
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval—0
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness—0
Evidence for a multi-level trophic organization of the human gut microbiomeCode0
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2nightCode0
Improving End-to-End Sequential Recommendations with Intent-aware DiversificationCode0
The many faces of deep learning—0
DGSAN: Discrete Generative Self-Adversarial NetworkCode0
Power Efficient Discontinuous Reception in THz and mmWave Wireless Systems—0
Self-reinforcing Unsupervised Matching—0
Neural Text Summarization: A Critical Evaluation—0
Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning—0
The effects of ecological selection on species diversity and trait distribution: predictions and an empirical test—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding DatasetsCode0
Dialog State Tracking with Reinforced Data Augmentation—0
Make a Face: Towards Arbitrary High Fidelity Face Manipulation—0
A Co-analysis Framework for Exploring Multivariate Scientific Data—0
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