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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

Showing 48264850 of 9051 papers

TitleStatusHype
KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants0
KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to Jailbreak LLMs0
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation0
KddRES: A Multi-level Knowledge-driven Dialogue Dataset for Restaurant Towards Customized Dialogue System0
Keeping Less is More: Point Sparsification for Visual SLAM0
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control0
Beyond Accuracy: Statistical Measures and Benchmark for Evaluation of Representation from Self-Supervised Learning0
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations0
Kernel-based Generative Learning in Distortion Feature Space0
Kernel Correlation-Dissimilarity for Multiple Kernel k-Means Clustering0
Kernel-Elastic Autoencoder for Molecular Design0
Kernel k-Medoids as General Vector Quantization0
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction0
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks0
K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning0
Towards Comprehensive Preference Data Collection for Reward Modeling0
KETG: A Knowledge Enhanced Text Generation Framework0
Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study0
Keyframe-Guided Creative Video Inpainting0
Keyphrase Generation with Correlation Constraints0
Better Alignment with Instruction Back-and-Forth Translation0
Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data0
A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency0
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer0
KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs0
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