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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 4426–4450 of 9051 papers

TitleStatusHype
Device-Free Localization Using Multi-Link MIMO Channels in Distributed Antenna Networks—0
Deviations from universality in the fluctuation behavior of a heterogeneous complex system reveal intrinsic properties of components: The case of the international currency market—0
Analyzing the Stability of Non-coplanar Circumbinary Planets using Machine Learning—0
Development of Multi-level Linguistic Alignment in Child-adult Conversations—0
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification—0
Information-theoretic interpretation of tuning curves for multiple motion directions—0
Infrared uplink design for visible light communication (VLC) systems with beam steering—0
Inhibitory loop robustly induces anticipated synchronization in neuronal microcircuits—0
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework—0
Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches—0
Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis—0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Achieving Distributed MIMO Performance with Repeater-Assisted Cellular Massive MIMO—0
BENCHIP: Benchmarking Intelligence Processors—0
A Dataset of Inertial Measurement Units for Handwritten English Alphabets—0
Determinantal consensus clustering—0
Determinantal Beam Search—0
BENCHAGENTS: Automated Benchmark Creation with Agent Interaction—0
AI for All: Identifying AI incidents Related to Diversity and Inclusion—0
Information Geometry for Maximum Diversity Distributions—0
Detection and Measurement of Syntactic Templates in Generated Text—0
Analyzing the Components of Distributed Coevolutionary GAN Training—0
IFDID: Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG—0
Detecting Trojaned DNNs Using Counterfactual Attributions—0
Detecting Sockpuppets in Deceptive Opinion Spam—0
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