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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 4251–4275 of 9051 papers

TitleStatusHype
DiMA: Sequence Diversity Dynamics Analyser for Viruses—0
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation—0
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules—0
Digital Life Project: Autonomous 3D Characters with Social Intelligence—0
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts—0
An Empirical Study of Diversity of Word Alignment and its Symmetrization Techniques for System Combination—0
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset—0
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving—0
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models—0
Variational Conditional GAN for Fine-grained Controllable Image Generation—0
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study—0
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models—0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research—0
Improved Training of Generative Adversarial Networks Using Representative Features—0
Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders—0
Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss—0
Improving negation detection with negation-focused pre-training—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
DiffusionPhase: Motion Diffusion in Frequency Domain—0
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering—0
Diffusion on language model encodings for protein sequence generation—0
Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions—0
Diffusion Model with Perceptual Loss—0
An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization—0
A Classification-Based Study of Covariate Shift in GAN Distributions—0
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