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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 6726–6750 of 9051 papers

TitleStatusHype
Echo Chambers in Collaborative Filtering Based Recommendation Systems—0
Provenance-Based Interpretation of Multi-Agent Information Analysis—0
Robustness and Diversity Seeking Data-Free Knowledge DistillationCode0
Testing the Impact of Semantics and Structure on Recommendation Accuracy and DiversityCode1
Blind Motion Deblurring through SinGAN Architecture—0
Physical Layer Security of Intelligent Reflective Surface Aided NOMA Networks—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Deep generative selection models of T and B cell receptor repertoires with soNNiaCode1
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and DiversityCode0
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Phylogenetic reconstruction of the cultural evolution of electronic music via dynamic community detection (1975-1999)Code0
Diversity-Enriched Option-CriticCode0
Diversity Aware Relevance Learning for Argument SearchCode0
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arraysCode1
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
Conditioned Text Generation with Transfer for Closed-Domain Dialogue SystemsCode1
Provenance-Based Assessment of Plans in Context—0
Classifier Pool Generation based on a Two-level Diversity Approach—0
Studying evolution of the primary body axis in vivo and in vitro—0
Distributed speech separation in spatially unconstrained microphone arraysCode1
The 2020s Political Economy of Machine Translation—0
Data-free Knowledge Distillation for Segmentation using Data-Enriching GANCode0
Diverse Image Captioning with Context-Object Split Latent SpacesCode1
Alibaba Submission to the WMT20 Parallel Corpus Filtering Task—0
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