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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 1276–1300 of 9051 papers

TitleStatusHype
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving—0
EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning—0
Crowdsourcing Lexical Diversity—0
OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction—0
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity—0
Topic-Conversation Relevance (TCR) Dataset and BenchmarksCode1
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks—0
GRADE: Quantifying Sample Diversity in Text-to-Image Models—0
FairSkin: Fair Diffusion for Skin Disease Image Generation—0
BENCHAGENTS: Automated Benchmark Creation with Agent Interaction—0
ReMix: Training Generalized Person Re-identification on a Mixture of Data—0
On the Role of Depth and Looping for In-Context Learning with Task Diversity—0
On the Statistical Complexity of Estimating VENDI Scores from Empirical Data—0
Can Users Detect Biases or Factual Errors in Generated Responses in Conversational Information-Seeking?Code0
Exploring the Design Space of Diffusion Bridge Models via Stochasticity Control—0
Reducing the Scope of Language Models—0
Semantic Component Analysis: Discovering Patterns in Short Texts Beyond TopicsCode0
ADLM -- stega: A Universal Adaptive Token Selection Algorithm for Improving Steganographic Text Quality via Information Entropy—0
Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review—0
Flaming-hot Initiation with Regular Execution Sampling for Large Language ModelsCode2
Neural Network-Enhanced Disease Spread Dynamics Over Time and Space—0
Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity—0
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning—0
Improving Model Evaluation using SMART Filtering of Benchmark DatasetsCode3
Optimizing Keyphrase Ranking for Relevance and Diversity Using Submodular Function Optimization (SFO)Code0
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