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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

Showing 76017625 of 9051 papers

TitleStatusHype
DAM: Diffusion Activation Maximization for 3D Global ExplanationsCode0
Blastocoel morphogenesis: a biophysics perspectiveCode0
Joint Training of Deep Ensembles Fails Due to Learner CollusionCode0
To Ensemble or Not Ensemble: When does End-To-End Training Fail?Code0
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy LearningCode0
Using Neural Networks and Diversifying Differential Evolution for Dynamic OptimisationCode0
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review GenerationCode0
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation LearningCode0
Enhancing Image Generation Fidelity via Progressive PromptsCode0
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic SegmentationCode0
JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language ProcessingCode0
Pantypes: Diverse Representatives for Self-Explainable ModelsCode0
Information Density Principle for MLLM BenchmarksCode0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-TranslationCode0
KappaFace: Adaptive Additive Angular Margin Loss for Deep Face RecognitionCode0
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
Simple Post-Training Robustness Using Test Time Augmentations and Random ForestCode0
SDIT: Scalable and Diverse Cross-domain Image TranslationCode0
Towards a Scalable Reference-Free Evaluation of Generative ModelsCode0
D3: Data Diversity Design for Systematic Generalization in Visual Question AnsweringCode0
Cyclic image generation using chaotic dynamicsCode0
Black-Box Testing of Deep Neural Networks Through Test Case DiversityCode0
Enhancing Feature Diversity Boosts Channel-Adaptive Vision TransformersCode0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
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