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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 4501–4525 of 9051 papers

TitleStatusHype
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving—0
NSOAMT -- New Search Only Approach to Machine Translation—0
Motif-Centric Representation Learning for Symbolic MusicCode0
GDM: Dual Mixup for Graph Classification with Limited Supervision—0
Interactions and migration rescuing ecological diversity—0
PanoMixSwap Panorama Mixing via Structural Swapping for Indoor Scene Understanding—0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
D3: Data Diversity Design for Systematic Generalization in Visual Question AnsweringCode0
Current and future directions in network biology—0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
Explaining Search Result Stances to Opinionated People—0
On Performance of Fluid Antenna System using Maximum Ratio Combining—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and CalibrationCode0
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion EnhancementCode0
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
Strong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation—0
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
Distributionally-Informed Recommender System Evaluation—0
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling—0
A variational selection mechanism for article comment generationCode0
Unveiling the Complexity of Neural Populations: Evaluating the Validity and Limitations of the Wilson-Cowan Model—0
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss—0
Exploring the Law of Numbers: Evidence from China's Real Estate—0
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