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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 226–250 of 9051 papers

TitleStatusHype
UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction ServicesCode2
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion modelsCode2
StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar GenerationCode2
gRNAde: Geometric Deep Learning for 3D RNA inverse designCode2
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time SeriesCode2
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language ModelsCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
SiLK -- Simple Learned KeypointsCode2
Ambiguous Medical Image Segmentation using Diffusion ModelsCode2
ReMoDiffuse: Retrieval-Augmented Motion Diffusion ModelCode2
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and ModelingCode2
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimizationCode2
Taming Diffusion Models for Audio-Driven Co-Speech Gesture GenerationCode2
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy PerceptionCode2
Streaming Active Learning with Deep Neural NetworksCode2
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous SpeechCode2
Effective Data Augmentation With Diffusion ModelsCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
Learning Video Representations from Large Language ModelsCode2
Scaling Language-Image Pre-training via MaskingCode2
Person Image Synthesis via Denoising Diffusion ModelCode2
SinDiffusion: Learning a Diffusion Model from a Single Natural ImageCode2
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