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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 5626–5650 of 9051 papers

TitleStatusHype
MixSyn: Learning Composition and Style for Multi-Source Image Synthesis—0
Few-shot Image Generation with Mixup-based Distance LearningCode1
The Impact of Data Distribution on Q-learning with Function ApproximationCode0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness—0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Delving into Rectifiers in Style-Based Image Translation—0
DVCFlow: Modeling Information Flow Towards Human-like Video Captioning—0
Expert-Guided Symmetry Detection in Markov Decision Processes—0
Resilience from Diversity: Population-based approach to harden models against adversarial attacksCode0
How to Build Robust FAQ Chatbot with Controllable Question Generator?—0
Constraint-based Diversification of JOP Gadgets—0
One-Shot Generative Domain Adaptation—0
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image RestorationCode1
Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population—0
DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation—0
Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach—0
Generating Diverse and High-Quality Abstractive Summaries with Variational Transformers—0
Text Complexity And Linguistic Features: Is The Relationship Multilingual?—0
How do people talk about images? A study on open-domain conversation on images.—0
Retrieval-guided Counterfactual Generation for QA—0
Enhancing Robustness of Pre-trained Language Model with Lexical Simplification—0
Assessing the Coherence Modeling Capabilities of Pretrained Transformer-based Language Models—0
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