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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 5276–5300 of 9051 papers

TitleStatusHype
Sim2real transfer learning for 3D human pose estimation: motion to the rescue—0
SimGen: Simulator-conditioned Driving Scene Generation—0
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition—0
Similarity-based Learning via Data Driven Embeddings—0
Similarity-Based Reconstruction Loss for Meaning Representation—0
Similarity of Information and Collective Action—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Simon @ LT-EDI-EACL2021: Detecting Hope Speech with BERT—0
Simple Regularisation for Uncertainty-Aware Knowledge Distillation—0
SimpleStrat: Diversifying Language Model Generation with Stratification—0
Simultaneously Learning Architectures and Features of Deep Neural Networks—0
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport—0
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach—0
SimVQA: Exploring Simulated Environments for Visual Question Answering—0
Since the Scientific Literature Is Multilingual, Our Models Should Be Too—0
Single-Antenna-Based GPS Antijamming Method Exploiting Polarization Diversity—0
Single-Cell Capsule Attention : an interpretable method of cell type classification for single-cell RNA-sequencing data—0
Single Domain Dynamic Generalization for Iris Presentation Attack Detection—0
Single Domain Generalization with Adversarial Memory—0
Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance—0
Single Ground Truth Is Not Enough: Add Linguistic Variability to Aspect-based Sentiment Analysis Evaluation—0
Single-Shot Phase Diversity Wavefront Sensing in Deep Turbulence via Metasurface Optics—0
Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data—0
SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene—0
Singular Value Penalization and Semantic Data Augmentation for Fully Test-Time Adaptation—0
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