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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

Showing 18011825 of 9051 papers

TitleStatusHype
Active Learning for Abstractive Text SummarizationCode0
Increasing Entropy to Boost Policy Gradient Performance on Personalization TasksCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
A Simple Method for Commonsense ReasoningCode0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
A Hierarchical Deep Learning Approach for Minority Instrument DetectionCode0
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender DiversityCode0
A Simple, Fast Diverse Decoding Algorithm for Neural GenerationCode0
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region FittingCode0
Computational detection of antigen specific B cell receptors following immunizationCode0
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point ProcessesCode0
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy LossCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
A Guide for Practical Use of ADMG Causal Data AugmentationCode0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-TranslationCode0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
A simple and effective hybrid genetic search for the job sequencing and tool switching problemCode0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
Improving Screening Processes via Calibrated Subset SelectionCode0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
A Grid-Based Evolutionary Algorithm for Many-Objective OptimizationCode0
Improving Language Generation with Sentence Coherence ObjectiveCode0
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