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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 29262950 of 9051 papers

TitleStatusHype
Dual-Representation Interaction Driven Image Quality Assessment with Restoration AssistanceCode0
Exploring Format Consistency for Instruction TuningCode0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
Exploring the Role of Diversity in Example Selection for In-Context LearningCode0
Exploratory State Representation LearningCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Exploiting ConvNet Diversity for Flooding IdentificationCode0
Explaining crime diversity with Google street viewCode0
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist ExaminationCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment ProblemCode0
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text GenerationCode0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble MannerCode0
Exact Fusion via Feature Distribution Matching for Few-shot Image GenerationCode0
Expanding functional protein sequence space using generative adversarial networksCode0
Deconditional Downscaling with Gaussian ProcessesCode0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context LearningCode0
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity AlgorithmCode0
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible TemplatesCode0
Exploring Diversity in Back Translation for Low-Resource Machine TranslationCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Decomposed Distribution Matching in Dataset CondensationCode0
Event Transition Planning for Open-ended Text GenerationCode0
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