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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
Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference—0
DOS: Diverse Outlier Sampling for Out-of-Distribution DetectionCode0
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
On Feature Diversity in Energy-based Models—0
KL-Divergence Guided Temperature SamplingCode0
Knowledge of cultural moral norms in large language modelsCode0
Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction—0
Survey of Trustworthy AI: A Meta Decision of AI—0
Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation—0
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset—0
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific DocumentsCode0
Adaptive Coordination in Social Embodied Rearrangement—0
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Generating Behaviorally Diverse Policies with Latent Diffusion Models—0
Compositional diversity in visual concept learning—0
Sparse species interactions reproduce abundance correlation patterns in microbial communities—0
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection—0
Runtime Analysis of Quality Diversity Algorithms—0
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching—0
Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation—0
Forgotten Knowledge: Examining the Citational Amnesia in NLP—0
Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender—0
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction—0
Just a Glimpse: Rethinking Temporal Information for Video Continual Learning—0
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