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 1826–1850 of 9051 papers

TitleStatusHype
A Grid-Based Evolutionary Algorithm for Many-Objective OptimizationCode0
AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question AnsweringCode0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy LossCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation SystemsCode0
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract DescriptionsCode0
LawDNet: Enhanced Audio-Driven Lip Synthesis via Local Affine Warping DeformationCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point ProcessesCode0
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-TuningCode0
Improving Language Generation with Sentence Coherence ObjectiveCode0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
A Benchmark Database of Phonetic Alignments in Historical Linguistics and DialectologyCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Improving Generalization with Domain Convex GameCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Controllable Motion Generation via Diffusion Modal CouplingCode0
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Complex Locomotion Skill Learning via Differentiable PhysicsCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
Show:102550
← PrevPage 74 of 363Next →

No leaderboard results yet.