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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 7001–7025 of 9051 papers

TitleStatusHype
A new flower pollination algorithm for equalization in synchronous DS/CDMA multiuser communication systems—0
Unsupervised Translation of Emergent Communication—0
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation—0
Relative Variational Intrinsic Control—0
Genealogical transition in the noisy N-Branching Random Walk. How stronger selection may promote genetic diversity—0
Relay Assisted OFDM with Subcarrier Number Modulation in Multi-Hop Cooperative Networks—0
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation—0
A new efficient Matching method for web services substitution—0
Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms—0
Unsupervised User-Based Insider Threat Detection Using Bayesian Gaussian Mixture Models—0
Unsupervised vocal dereverberation with diffusion-based generative models—0
Relevance-Promoting Language Model for Short-Text Conversation—0
Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback—0
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity—0
A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior—0
A new dataset of dog breed images and a benchmark for fine-grained classification—0
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification—0
A New Data Normalization Method to Improve Dialogue Generation by Minimizing Long Tail Effect—0
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction—0
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing—0
ReMix: Training Generalized Person Re-identification on a Mixture of Data—0
A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain—0
REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models—0
A Neural Compositional Paradigm for Image Captioning—0
A Neural Approach to Pun Generation—0
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