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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 7551–7575 of 9051 papers

TitleStatusHype
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-EncodersCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Zero-Shot Paraphrase Generation with Multilingual Language Models—0
Conditioned Query Generation for Task-Oriented Dialogue SystemsCode1
Automatic Identification of Traditional Colombian Music Genres based on Audio Content Analysis and Machine Learning TechniqueCode0
Probing Contextualized Sentence Representations with Visual Awareness—0
Ensembles of Locally Independent Prediction ModelsCode0
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning—0
Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering—0
Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data—0
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification—0
Hierarchy Response Learning for Neural Conversation Generation—0
Exploring Diverse Expressions for Paraphrase Generation—0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
Multilingual Whispers: Generating Paraphrases with Translation—0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-TranslationCode0
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction—0
Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities—0
Dreaddit: A Reddit Dataset for Stress Analysis in Social MediaCode0
SignCol: Open-Source Software for Collecting Sign Language GesturesCode0
Predicting the Politics of an Image Using Webly Supervised DataCode0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Not even wrong: Reply to Wagg et al—0
When does Diversity Help Generalization in Classification Ensembles?—0
Unsupervised inference approach to facial attractiveness—0
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