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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 4801–4825 of 9051 papers

TitleStatusHype
PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling—0
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication—0
Joint Event Detection and Entity Resolution: a Virtuous Cycle—0
Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms—0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes—0
Jointly Optimizing Diversity and Relevance in Neural Response Generation—0
Jointly Understand Your Command and Intention:Reciprocal Co-Evolution between Scene-Aware 3D Human Motion Synthesis and Analysis—0
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization—0
Joint Passage Ranking for Diverse Multi-Answer Retrieval—0
Joint Summarization of Large-scale Collections of Web Images and Videos for Storyline Reconstruction—0
Towards Automatic Gesture Stroke Detection—0
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection—0
Zonotope hit-and-run for efficient sampling from projection DPPs—0
JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking—0
Towards Bridging the Digital Language Divide—0
Just Add $100 More: Augmenting NeRF-based Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem—0
Just a Glimpse: Rethinking Temporal Information for Video Continual Learning—0
Just Say the Name: Online Continual Learning with Category Names Only via Data Generation—0
Towards building a Robust Industry-scale Question Answering System—0
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling—0
Beyond Blur: A Fluid Perspective on Generative Diffusion Models—0
KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity—0
Towards complete representation of bacterial contents in metagenomic samples—0
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