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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 5976–6000 of 9051 papers

TitleStatusHype
When One LLM Drools, Multi-LLM Collaboration Rules—0
New Evaluation Metrics Capture Quality Degradation due to LLM Watermarking—0
New expressions on the performance of a novel multi-hop relay-assisted hybrid FSO / RF communication system with receive diversity—0
A Classification-Based Study of Covariate Shift in GAN Distributions—0
When should agents explore?—0
New thoughts on an old riddle: what determines genetic diversity within and between species?—0
Niche differentiation in the light spectrum promotes coexistence of phytoplankton species: a spatial modelling approach—0
A Classification-Based Perspective on GAN Distributions—0
Niching Diversity Estimation for Multi-modal Multi-objective Optimization—0
NightCC: Nighttime Color Constancy via Adaptive Channel Masking—0
Nitrogen-induced hysteresis in grassland biodiversity: a theoretical test of litter-mediated mechanisms—0
A small Griko-Italian speech translation corpus—0
A Small but Informed and Diverse Model: The Case of the Multimodal GuessWhat!? Guessing Game—0
Language Varieties of Italy: Technology Challenges and Opportunities—0
UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space Translation—0
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World—0
A Chinese Continuous Sign Language Dataset Based on Complex Environments—0
NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors—0
No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models—0
As long as you talk about me: The importance of family firm brands and the contingent role of family-firm identity—0
Noise Consistency Regularization for Improved Subject-Driven Image Synthesis—0
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning—0
Ask to Understand: Question Generation for Multi-hop Question Answering—0
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
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