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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 3026–3050 of 9051 papers

TitleStatusHype
Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR—0
Efficacy of Machine-Generated Instructions—0
Efficient aggregation of face embeddings for decentralized face recognition deployments (extended version)—0
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers—0
Evaluating the diversity and utility of materials proposed by generative models—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning—0
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems—0
Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detection—0
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors—0
Efficiently Secure Broadcasting in 5G Wireless Fog-Based-Fronthaul Networks—0
Efficient Medical VIE via Reinforcement Learning—0
Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Efficient nonmyopic batch active search—0
Diversity as a By-Product: Goal-oriented Language Generation Leads to Linguistic Variation—0
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer—0
An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems—0
Cifu: a Frequency Lexicon of Hong Kong Cantonese—0
Evaluating the Diversity and Quality of LLM Generated Content—0
Efficient Sampling for k-Determinantal Point Processes—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
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