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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 5151–5175 of 9051 papers

TitleStatusHype
LED: Latent Variable-based Estimation of Density—0
Towards Markerless Grasp Capture—0
A cross-study analysis of drug response prediction in cancer cell lines—0
Axiomatic Explainer Globalness via Optimal Transport—0
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks—0
Lesion-Aware Transformers for Diabetic Retinopathy Grading—0
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models—0
Less data is more: Selecting informative and diverse subsets with balancing constraints—0
Less is More: Efficient Point Cloud Reconstruction via Multi-Head Decoders—0
A weighted quantum ensemble of homogeneous quantum classifiers—0
Less Is More: Picking Informative Frames for Video Captioning—0
Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study—0
Lessons to learn for better safeguarding of genetic resources during tree pandemics: the case of ash dieback in Europe—0
Let Me Teach You: Pedagogical Foundations of Feedback for Language Models—0
LetsTalk: Latent Diffusion Transformer for Talking Video Synthesis—0
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization—0
A Weather-Dependent Hybrid RF/FSO Satellite Communication for Improved Power Efficiency—0
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design—0
A Wasserstein GAN model with the total variational regularization—0
Leveraging band diversity for feature selection in EO data—0
Leveraging Contextual Counterfactuals Toward Belief Calibration—0
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition—0
Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks—0
Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking—0
Leveraging Diversity in Online Interactions—0
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