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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 6551–6575 of 9051 papers

TitleStatusHype
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing—0
Probabilistic Inference for Learning from Untrusted Sources—0
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation—0
Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception—0
MLGO: a Machine Learning Guided Compiler Optimizations FrameworkCode1
Fine-grained Semantic Constraint in Image Synthesis—0
Multi-Domain Image-to-Image Translation with Adaptive Inference Graph—0
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object DetectionCode1
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks—0
Analyzing the Stability of Non-coplanar Circumbinary Planets using Machine Learning—0
Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN—0
Learning Guided Electron Microscopy with Active AcquisitionCode0
Deep Reinforcement Learning with Quantum-inspired Experience Replay—0
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background—0
VersatileGait: A Large-Scale Synthetic Gait Dataset with Fine-GrainedAttributes and Complicated Scenarios—0
Learning from Synthetic Shadows for Shadow Detection and RemovalCode1
Recommending Accurate and Diverse Items Using Bilateral Branch Network—0
Coreference Resolution: Are the eliminated spans totally worthless?—0
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge DistillationCode1
Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation—0
Diverse Image Style Transfer via Invertible Cross-Space Mapping—0
Out-of-Core Surface Reconstruction via Global TGV Minimization—0
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel LossCode1
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Channel Augmented Joint Learning for Visible-Infrared RecognitionCode0
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