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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

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Showing 601–625 of 9051 papers

TitleStatusHype
SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite ImagesCode1
BehAVE: Behaviour Alignment of Video Game EncodingsCode1
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity RecognitionCode1
LLM Voting: Human Choices and AI Collective Decision MakingCode1
On the Affinity, Rationality, and Diversity of Hierarchical Topic ModelingCode1
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
ARGS: Alignment as Reward-Guided SearchCode1
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Learning High-Quality and General-Purpose Phrase RepresentationsCode1
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender SystemsCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion ProcessCode1
Ensemble Diversity Facilitates Adversarial TransferabilityCode1
HomoFormer: Homogenized Transformer for Image Shadow RemovalCode1
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster MemoryCode1
ODAQ: Open Dataset of Audio QualityCode1
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs ResponsesCode1
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3DCode1
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide GenerationCode1
Quality-Diversity Generative Sampling for Learning with Synthetic DataCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
De novo Drug Design using Reinforcement Learning with Multiple GPT AgentsCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
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