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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 626–650 of 9051 papers

TitleStatusHype
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
Deep Ordinal Regression with Label DiversityCode1
An Extensible Benchmark Suite for Learning to Simulate Physical SystemsCode1
Deep Sketch-Based Modeling: Tips and TricksCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
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