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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 9026–9050 of 9051 papers

TitleStatusHype
Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples—0
Text-Conditional Contextualized Avatars For Zero-Shot Personalization—0
Enforcing Structural Diversity in Cube-pruned Dependency Parsing—0
Enforcing Topic Diversity in a Document Recommender for Conversations—0
Engineering Artificial Intelligence: Framework, Challenges, and Future Direction—0
English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System—0
English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs—0
Enhanced 3DMM Attribute Control via Synthetic Dataset Creation Pipeline—0
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances—0
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language—0
Enhanced Optimization with Composite Objectives and Novelty Selection—0
Enhanced Optimization with Composite Objectives and Novelty Pulsation—0
Enhanced Recommendation Combining Collaborative Filtering and Large Language Models—0
Enhanced Spatially Interleaved Techniques for Multi-View Distributed Video Coding—0
Enhanced species coexistence in Lotka-Volterra competition models due to nonlocal interactions—0
Coherent FDA Radar: Transmitter and Receiver Design and Analysis—0
Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation—0
Enhance Robustness of Language Models Against Variation Attack through Graph Integration—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
Enhancing Adversarial Transferability with Checkpoints of a Single Model's Training—0
Enhancing Age-Related Robustness in Children Speaker Verification—0
Enhancing Annotated Bibliography Generation with LLM Ensembles—0
TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering—0
Enhancing Audio Augmentation Methods with Consistency Learning—0
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