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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 6251–6275 of 9051 papers

TitleStatusHype
Stochastic Image-to-Video Synthesis using cINNsCode1
Exploring open-ended gameplay features with Micro RollerCoaster Tycoon—0
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input ModalitiesCode1
Designing Air Flow with Surrogate-assisted Phenotypic Niching—0
FAID Diversity via Neural Networks—0
An Analysis of Phenotypic Diversity in Multi-Solution Optimization—0
Expressivity of Parameterized and Data-driven Representations in Quality Diversity SearchCode0
English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System—0
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and SamplingCode0
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of ExpertsCode1
Semantics in Multi-objective Genetic Programming—0
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose EstimationCode1
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble MethodsCode0
Capturing the diversity of multilingual societiesCode0
On the Ethical Limits of Natural Language Processing on Legal Text—0
PD-GAN: Probabilistic Diverse GAN for Image InpaintingCode1
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution—0
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment—0
Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms—0
The Tracking Machine Learning challenge : Throughput phaseCode1
Subspace Representation Learning for Few-shot Image Classification—0
Discovering Diverse Athletic Jumping Strategies—0
Why scholars are diagramming neural network models—0
Few-Shot Video Object DetectionCode1
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