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

Showing 84518475 of 9051 papers

TitleStatusHype
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesCode0
Flexible Modeling of Diversity with Strongly Log-Concave DistributionsCode0
Modelling the effect of within-host dynamics on the diversity of a multi-strain pathogenCode0
A Learned Representation For Artistic StyleCode0
Wav2Gloss: Generating Interlinear Glossed Text from SpeechCode0
Model Selection with Model Zoo via Graph LearningCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Mode Seeking Generative Adversarial Networks for Diverse Image SynthesisCode0
ArchiGuesser -- AI Art Architecture Educational GameCode0
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI ModelsCode0
ARBERT \& MARBERT: Deep Bidirectional Transformers for ArabicCode0
Albumentations: fast and flexible image augmentationsCode0
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
Unseen Object Reasoning with Shared Appearance CuesCode0
Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement LearningCode0
Randomness Is All You Need: Semantic Traversal of Problem-Solution Spaces with Large Language ModelsCode0
Specify and Edit: Overcoming Ambiguity in Text-Based Image EditingCode0
Training for Diversity in Image Paragraph CaptioningCode0
Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex Driving ScenesCode0
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert SpecializationCode0
First U-Net Layers Contain More Domain Specific Information Than The Last OnesCode0
CoCoFormer: A controllable feature-rich polyphonic music generation methodCode0
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped usersCode0
Ranking In Generalized Linear BanditsCode0
Show:102550
← PrevPage 339 of 363Next →

No leaderboard results yet.