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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 7151–7175 of 9051 papers

TitleStatusHype
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services—0
r/K selection of GC content in prokaryotes—0
RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning—0
RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse—0
Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction—0
User Modeling for Task Oriented Dialogues—0
Analyzing the Stability of Non-coplanar Circumbinary Planets using Machine Learning—0
RNE: A Scalable Network Embedding for Billion-scale Recommendation—0
Road detection via a dual-task network based on cross-layer graph fusion modules—0
User-Movement-Robust Virtual Reality Through Dual-Beam Reception in mmWave Networks—0
Analyzing the Components of Distributed Coevolutionary GAN Training—0
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs—0
RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation—0
RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis and Transfer—0
Robo-Troj: Attacking LLM-based Task Planners—0
Robust Allocations with Diversity Constraints—0
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text—0
Robust Deep Learning Ensemble against Deception—0
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack—0
Robust Federated Learning on Edge Devices with Domain Heterogeneity—0
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity—0
Robust Fraud Detection via Supervised Contrastive Learning—0
Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study—0
Robust Invariant Representation Learning by Distribution Extrapolation—0
Robust Memory Augmentation by Constrained Latent Imagination—0
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