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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 4401–4425 of 9051 papers

TitleStatusHype
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control—0
An Empirical Study of Translation Hypothesis Ensembling with Large Language ModelsCode0
Private Synthetic Data Meets Ensemble Learning—0
CoCoFormer: A controllable feature-rich polyphonic music generation methodCode0
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack—0
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer—0
ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models—0
Graph Neural Network approaches for single-cell data: A recent overview—0
Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration—0
Incentive Mechanism Design for Distributed Ensemble Learning—0
Dialect Transfer for Swiss German Speech Translation—0
Evolutionary Dynamic Optimization and Machine Learning—0
Analysing of 3D MIMO Communication Beamforming in Linear and Planar Arrays—0
Kernel-Elastic Autoencoder for Molecular Design—0
FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning—0
Context-Enhanced Detector For Building Detection From Remote Sensing Images—0
Diversity of Thought Improves Reasoning Abilities of LLMs—0
Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer—0
On the Impact of Cross-Domain Data on German Language Models—0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
Mitigating stereotypical biases in text to image generative systems—0
RK-core: An Established Methodology for Exploring the Hierarchical Structure within DatasetsCode0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
Diversity from Human Feedback—0
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQACode0
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