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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 4751–4775 of 9051 papers

TitleStatusHype
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target ImputationCode0
Chaotic turnover of rare and abundant species in a strongly interacting model community—0
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Optimal Execution Using Reinforcement Learning—0
Perturbation-Based Two-Stage Multi-Domain Active Learning—0
Personalized Elastic Embedding Learning for On-Device Recommendation—0
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text GenerationCode0
Differentiable Instruction Optimization for Cross-Task GeneralizationCode0
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High DimensionsCode0
On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search—0
The Core & Periphery Hypothesis: A Conceptual Basis for Generality in Cell and Developmental Biology—0
A9 Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception—0
Data Augmentation for Seizure Prediction with Generative Diffusion Model—0
DiffuDetox: A Mixed Diffusion Model for Text Detoxification—0
BRUDEX Database: Binaural Room Impulse Responses with Uniformly Distributed External Microphones—0
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings—0
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts—0
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning—0
Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity—0
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation—0
Generating Language Corrections for Teaching Physical Control TasksCode0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
On the Amplification of Linguistic Bias through Unintentional Self-reinforcement Learning by Generative Language Models -- A Perspective—0
Augmenting Zero-Shot Detection Training with Image Labels—0
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