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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 4726–4750 of 9051 papers

TitleStatusHype
Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction—0
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
SQuId: Measuring Speech Naturalness in Many Languages—0
Measuring and Improving Semantic Diversity of Dialogue GenerationCode0
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Habitat-Matterport 3D Semantics Dataset—0
The quest for the definition of life—0
CORE: A Retrieve-then-Edit Framework for Counterfactual Data GenerationCode0
EVA3D: Compositional 3D Human Generation from 2D Image CollectionsCode2
Semi-supervised Semantic Segmentation with Prototype-based Consistency RegularizationCode1
SCAM! Transferring humans between images with Semantic Cross Attention ModulationCode1
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors—0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis—0
Take a Fresh Look at Recommender Systems from an Evaluation Standpoint—0
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation LearningCode0
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPUCode0
Training Deep Learning Algorithms on Synthetic Forest Images for Tree DetectionCode1
STaSy: Score-based Tabular data SynthesisCode1
Winner Takes It All: Training Performant RL Populations for Combinatorial OptimizationCode1
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
Automatic Chain of Thought Prompting in Large Language ModelsCode6
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation—0
Generative Augmented Flow Networks—0
Dynamic Latent Separation for Deep Learning—0
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