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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 101125 of 9051 papers

TitleStatusHype
GroundingSuite: Measuring Complex Multi-Granular Pixel GroundingCode2
ProtComposer: Compositional Protein Structure Generation with 3D EllipsoidsCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
Delta Decompression for MoE-based LLMs CompressionCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
Knowledge Graph-Guided Retrieval Augmented GenerationCode2
Compressed Image Generation with Denoising Diffusion Codebook ModelsCode2
Diverse Preference OptimizationCode2
General Scene Adaptation for Vision-and-Language NavigationCode2
Visual Generation Without GuidanceCode2
Test-time Alignment of Diffusion Models without Reward Over-optimizationCode2
Magic Mirror: ID-Preserved Video Generation in Video Diffusion TransformersCode2
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented InstructionsCode2
RecLM: Recommendation Instruction TuningCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
AnySat: One Earth Observation Model for Many Resolutions, Scales, and ModalitiesCode2
Guiding Generative Protein Language Models with Reinforcement LearningCode2
EvalGIM: A Library for Evaluating Generative Image ModelsCode2
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data GenerationCode2
Find Any Part in 3DCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Learning General-Purpose Biomedical Volume Representations using Randomized SynthesisCode2
EgoMimic: Scaling Imitation Learning via Egocentric VideoCode2
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