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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 2751–2775 of 9051 papers

TitleStatusHype
Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future—0
TroubleLLM: Align to Red Team Expert—0
RORA: Robust Free-Text Rationale EvaluationCode0
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction—0
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality EstimationCode2
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScoreCode0
Improving Open-Ended Text Generation via Adaptive DecodingCode1
Priority Sampling of Large Language Models for Compilers—0
Autoencoder-based General Purpose Representation Learning for Customer Embedding—0
Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble—0
SoFA: Shielded On-the-fly Alignment via Priority Rule FollowingCode0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Image-Text Matching with Multi-View Attention—0
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-TuningCode1
DivAvatar: Diverse 3D Avatar Generation with a Single Prompt—0
RECOST: External Knowledge Guided Data-efficient Instruction Tuning—0
Federated Learning for Estimating Heterogeneous Treatment Effects—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward EncodingsCode2
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data—0
TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics—0
DiffuCOMET: Contextual Commonsense Knowledge DiffusionCode0
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower ResolutionsCode1
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning—0
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts—0
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