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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 6651–6675 of 9051 papers

TitleStatusHype
Does the dataset meet your expectations? Explaining sample representation in image data—0
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients—0
Energy-Efficient Design of Broad Beams for Massive MIMO Systems—0
Few-shot Image Generation with Elastic Weight Consolidation—0
Forecast with Forecasts: Diversity Matters—0
Detecting Trojaned DNNs Using Counterfactual Attributions—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Federated Learning for Personalized Humor Recognition—0
Entropy and Diversity: The Axiomatic Approach—0
Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training—0
Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions—0
Extractive Financial Narrative Summarisation based on DPPs—0
Named-Entity Based Sentiment Analysis of Nepali News Media Texts—0
Leveraging Latent Representations of Speech for Indian Language Identification—0
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation—0
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition—0
Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems—0
Towards building a Robust Industry-scale Question Answering System—0
Diverse dialogue generation with context dependent dynamic loss function—0
Offensive Language Detection on Video Live Streaming Chat—0
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation—0
Generalized Shortest-Paths Encoders for AMR-to-Text Generation—0
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation—0
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music CommentCode0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
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