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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 3726–3750 of 9051 papers

TitleStatusHype
The impact of data set similarity and diversity on transfer learning success in time series forecasting—0
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning—0
In-Flight Estimation of Instrument Spectral Response Functions Using Sparse Representations—0
Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population—0
PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?—0
Dynamic Quality-Diversity SearchCode0
Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems—0
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble MannerCode0
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications—0
Model Selection with Model Zoo via Graph LearningCode0
Robust Few-Shot Ensemble Learning with Focal Diversity-Based PruningCode0
LCM-Lookahead for Encoder-based Text-to-Image Personalization—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity PragmaticsCode0
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning—0
Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation—0
InsectMamba: Insect Pest Classification with State Space Model—0
On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative ComprehensionCode0
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison—0
On the Scalability of Diffusion-based Text-to-Image Generation—0
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes—0
DIBS: Enhancing Dense Video Captioning with Unlabeled Videos via Pseudo Boundary Enrichment and Online Refinement—0
Voice EHR: Introducing Multimodal Audio Data for Health—0
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image—0
Corpus Considerations for Annotator Modeling and ScalingCode0
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