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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 3001–3050 of 9051 papers

TitleStatusHype
EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation—0
EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics—0
Evaluation of Faithfulness Using the Longest Supported Subsequence—0
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue—0
Evaluation of large-scale synthetic data for Grammar Error Correction—0
Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch—0
Evolutionary Demographic Algorithms—0
Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations—0
Effective Dynamics of Generative Adversarial Networks—0
Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization—0
Exact Sampling of Determinantal Point Processes without Eigendecomposition—0
Effective Social Chatbot Strategies for Increasing User Initiative—0
Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation—0
Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)—0
Effective Visualization and Analysis of Recommender Systems—0
Effect of delay on the emergent stability patterns in Generalized Lotka-Volterra ecological dynamics—0
Effect of Gender Fair Job Description on Generative AI Images—0
Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks—0
Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity—0
Effects of a pestilent species on the stability of cyclically dominant species—0
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation—0
Effects of discordance between species and gene trees on phylogenetic diversity conservation—0
Chirped DFT-s-OFDM: A new single-carrier waveform with enhanced LMMSE noise suppression—0
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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