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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 701725 of 9051 papers

TitleStatusHype
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping DatasetsCode1
Domain-specific ChatBots for Science using EmbeddingsCode1
DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language ModelsCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
New Protocols and Negative Results for Textual Entailment Data CollectionCode1
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Class-Balancing Diffusion ModelsCode1
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
Answering Ambiguous Questions via Iterative PromptingCode1
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
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