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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 18111820 of 9051 papers

TitleStatusHype
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy LossCode0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-TranslationCode0
A Guide for Practical Use of ADMG Causal Data AugmentationCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
A simple and effective hybrid genetic search for the job sequencing and tool switching problemCode0
LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR SystemsCode0
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-TuningCode0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
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