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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 2626–2650 of 9051 papers

TitleStatusHype
5G framework concepts for the next generation networks—0
Is News Recommendation a Sequential Recommendation Task?—0
A Novel Detection Algorithm Efficient for Turbo coded CDMA Signals in Detect and Forward Cooperative Channels—0
Adversarial Bootstrapping for Dialogue Model Training—0
Can time series forecasting be automated? A benchmark and analysis—0
Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?—0
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography—0
Dynamic Post-Hoc Neural Ensemblers—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models—0
A Novel Cross-Perturbation for Single Domain Generalization—0
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers—0
Dynamic Range Independent Image Quality Assessment—0
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires—0
DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI—0
Echo Chambers in Collaborative Filtering Based Recommendation Systems—0
Can Prompting LLMs Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness—0
Canny2Palm: Realistic and Controllable Palmprint Generation for Large-scale Pre-training—0
Adversarial attacks to image classification systems using evolutionary algorithms—0
A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding—0
Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task—0
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