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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 6451–6475 of 9051 papers

TitleStatusHype
Syntactic and Semantic-driven Learning for Open Information ExtractionCode0
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup—0
Diversity Based Edge Pruning of Neural Networks Using Determinantal Point Processes—0
Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy—0
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference—0
Ensembles of Random SHAPs—0
Nutrition5k: Towards Automatic Nutritional Understanding of Generic FoodCode1
When Face Recognition Meets Occlusion: A New BenchmarkCode1
Toward Robust Long Range Policy Transfer—0
Advances in Multi-turn Dialogue Comprehension: A Survey—0
Group-wise Inhibition based Feature Regularization for Robust ClassificationCode1
GLAMOUR: Graph Learning over Macromolecule RepresentationsCode1
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition—0
Neuronal heterogeneity modulates phase-synchronization between unidirectionally coupled populations with excitation-inhibition balance—0
Towards Efficiently Diversifying Dialogue Generation via Embedding AugmentationCode0
Multi-Objective Evolutionary Design of Composite Data-Driven ModelsCode1
Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition—0
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation—0
Domain Adaptation for Learning Generator from Paired Few-Shot Data—0
Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder—0
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial NetworksCode1
Analysis of Evolutionary Diversity Optimisation for Permutation Problems—0
QuPeL: Quantized Personalization with Applications to Federated Learning—0
Controllable and Diverse Text Generation in E-commerce—0
Pre-Training BERT on Arabic Tweets: Practical Considerations—0
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