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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 4851–4875 of 9051 papers

TitleStatusHype
Co-Learning Empirical Games and World Models—0
Active Learning Principles for In-Context Learning with Large Language Models—0
GrACE: Generation using Associated Code Edits—0
Curse of "Low" Dimensionality in Recommender Systems—0
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLPCode0
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database—0
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
Sensing Diversity and Sparsity Models for Event Generation and Video Reconstruction from EventsCode0
Diversity and Inclusion in Artificial Intelligence—0
Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation—0
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist ExaminationCode0
Single Domain Dynamic Generalization for Iris Presentation Attack Detection—0
On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows—0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
LMGQS: A Large-scale Dataset for Query-focused Summarization—0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
A study of conceptual language similarity: comparison and evaluation—0
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification—0
Multi-factor Sequential Re-ranking with Perception-Aware Diversification—0
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies—0
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data—0
Enhancing biodiversity through intraspecific suppression in large ecosystems—0
Modeling the Q-Diversity in a Min-max Play Game for Robust OptimizationCode0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
DiffCap: Exploring Continuous Diffusion on Image Captioning—0
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