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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 2526–2550 of 9051 papers

TitleStatusHype
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space—0
Addressing the Abstraction and Reasoning Corpus via Procedural Example GenerationCode3
Exploring Diverse Sounds: Identifying Outliers in a Music Corpus—0
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
PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?—0
Multi-head Attention-based Deep Multiple Instance LearningCode1
Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population—0
Dynamic Quality-Diversity SearchCode0
Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems—0
Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin TonesCode1
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble MannerCode0
Robust Few-Shot Ensemble Learning with Focal Diversity-Based PruningCode0
Model Selection with Model Zoo via Graph LearningCode0
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications—0
Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation—0
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning—0
Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity PragmaticsCode0
LCM-Lookahead for Encoder-based Text-to-Image Personalization—0
InsectMamba: Insect Pest Classification with State Space Model—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language ModelsCode1
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison—0
DIBS: Enhancing Dense Video Captioning with Unlabeled Videos via Pseudo Boundary Enrichment and Online Refinement—0
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