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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 5801–5850 of 9051 papers

TitleStatusHype
Improving Neural Network Generalization via Promoting Within-Layer Diversity—0
Improving Out-of-Distribution Robustness of Classifiers Through Interpolated Generative Models—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
Single-Cell Capsule Attention : an interpretable method of cell type classification for single-cell RNA-sequencing data—0
Synaptic Diversity in ANNs Can Facilitate Faster Learning—0
What Makes for Good Representations for Contrastive Learning—0
Enhancing the Transferability of Adversarial Attacks via Scale Ensemble—0
Reconstructing Word Embeddings via Scattered k-Sub-Embedding—0
Deep Ensemble Policy Learning—0
Domain-wise Adversarial Training for Out-of-Distribution Generalization—0
Diverse and Consistent Multi-view Networks for Semi-supervised Regression—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
SANE: Specialization-Aware Neural Network Ensemble—0
Perturbation Diversity Certificates Robust Generalisation—0
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space—0
CausalDyna: Improving Generalization of Dyna-style Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Unconditional Diffusion Guidance—0
Maximizing Ensemble Diversity in Deep Reinforcement Learning—0
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning—0
Generalization to Out-of-Distribution transformations—0
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture GenerationCode1
Less data is more: Selecting informative and diverse subsets with balancing constraints—0
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation—0
Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity—0
Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean—0
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal GraphsCode0
Making Curiosity Explicit in Vision-based RL—0
Exploratory State Representation LearningCode0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Audio-to-Image Cross-Modal Generation—0
Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)Code0
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles—0
Review of Clustering-Based Recommender Systems—0
Parallel Refinements for Lexically Constrained Text Generation with BARTCode1
Smartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence—0
Stacked Ensemble Machine Learning for Range-Separation ParametersCode0
Human genetic admixture through the lens of population genomics—0
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification—0
Internal Feedback in Biological Control: Diversity, Delays, and Standard Theory—0
The Beauty Everywhere: How Aesthetic Criteria Contribute to the Development of AI—0
Stigmergy-based collision-avoidance algorithm for self-organising swarms—0
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-IdentificationCode1
Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management—0
Search For Deep Graph Neural Networks—0
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation—0
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision—0
Variational Embedding Multiscale Sample Entropy:complexity-based analysis for multichannel systems—0
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