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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 1211–1220 of 9051 papers

TitleStatusHype
ViTOC: Vision Transformer and Object-aware Captioner—0
Quasi-random Multi-Sample Inference for Large Language Models—0
MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems—0
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule GenerationCode0
STARS: Sensor-agnostic Transformer Architecture for Remote Sensing—0
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Generating Highly Designable Proteins with Geometric Algebra Flow MatchingCode1
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting DiversityCode0
A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior—0
One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversityCode0
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