SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5801–5825 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
Show:102550
← PrevPage 233 of 363Next →

No leaderboard results yet.