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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 5576–5600 of 9051 papers

TitleStatusHype
Temporal Regularization Makes Your Video Generator Stronger—0
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation—0
Ten computational challenges in human virome studies—0
Tensor Decomposition based Personalized Federated Learning—0
Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks—0
Testing Determinantal Point Processes—0
Testing High-dimensional Multinomials with Applications to Text Analysis—0
Testing macroecological theories in cryptocurrency market: neutral models can not describe diversity patterns and their variation—0
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning—0
Test-Optional Admissions—0
Test-time Adaptation for Cross-modal Retrieval with Query Shift—0
Test-Time Intensity Consistency Adaptation for Shadow Detection—0
Text2Immersion: Generative Immersive Scene with 3D Gaussians—0
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization—0
Text Complexity And Linguistic Features: Is The Relationship Multilingual?—0
Text-Conditional Contextualized Avatars For Zero-Shot Personalization—0
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language—0
TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering—0
Text-Driven Diverse Facial Texture Generation via Progressive Latent-Space Refinement—0
Text-driven Human Motion Generation with Motion Masked Diffusion Model—0
TextGAIL: Generative Adversarial Imitation Learning for Text Generation—0
Text Generation with Deep Variational GAN—0
Text-guided Diffusion Model for 3D Molecule Generation—0
Text-guided Explorable Image Super-resolution—0
Text Image Generation for Low-Resource Languages with Dual Translation Learning—0
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