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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 751–775 of 9051 papers

TitleStatusHype
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization—0
Trajectory World Models for Heterogeneous EnvironmentsCode1
Latent Lexical Projection in Large Language Models: A Novel Approach to Implicit Representation Refinement—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting—0
SliderSpace: Decomposing the Visual Capabilities of Diffusion ModelsCode0
Compressed Image Generation with Denoising Diffusion Codebook ModelsCode2
The Impact of Persona-based Political Perspectives on Hateful Content Detection—0
Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework—0
Multilingual State Space Models for Structured Question Answering in Indic LanguagesCode0
Dominated Novelty Search: Rethinking Local Competition in Quality-DiversityCode0
In Pursuit of Predictive Models of Human Preferences Toward AI Teammates—0
Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations—0
Language Games as the Pathway to Artificial Superhuman Intelligence—0
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domainsCode1
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition—0
Improving vision-language alignment with graph spiking hybrid Networks—0
One Stack, Diverse Vehicles: Checking Safe Portability of Automated Driving Software—0
Diversity By Design: Leveraging Distribution Matching for Offline Model-Based OptimizationCode0
Diverse Preference OptimizationCode2
Synthetic Data Generation for Augmenting Small Samples—0
Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations—0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programming—0
Accelerated DC loadflow solver for topology optimization—0
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