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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 5076–5100 of 9051 papers

TitleStatusHype
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization—0
Toward Robust Uncertainty Estimation with Random Activation FunctionsCode0
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?—0
Finding Support Examples for In-Context Learning—0
Cross-modal Face- and Voice-style Transfer—0
Diversity matters: Robustness of bias measurements in WikidataCode0
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets—0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric MaterialsCode0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
Medical visual question answering using joint self-supervised learning—0
Tractable Diversity: Scalable Multiperspective Ontology Management via Standpoint EL—0
Vagueness in Predicates and Objects—0
Lasting Diversity and Superior Runtime Guarantees for the (μ+1) Genetic Algorithm—0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across LanguagesCode0
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model MarketCode0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Learning from Multiple Sources for Data-to-Text and Text-to-DataCode0
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship—0
Cyclic Delay-Doppler Shift: A Simple Transmit Diversity Technique for Delay-Doppler Waveforms in Doubly Selective Channels—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
Feasible Recourse Plan via Diverse InterpolationCode0
Texturize a GAN Using a Single Image—0
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