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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 1576–1600 of 9051 papers

TitleStatusHype
Fork or Fail: Cycle-Consistent Training with Many-to-One MappingsCode1
New Protocols and Negative Results for Textual Entailment Data CollectionCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption GenerationCode1
Fractal Autoencoders for Feature SelectionCode1
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
Invariant Feature Regularization for Fair Face RecognitionCode1
Item-based Variational Auto-encoder for Fair Music RecommendationCode1
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge BaseCode1
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and ClassificationCode1
On Pretraining Data Diversity for Self-Supervised LearningCode1
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain RemovalCode1
Style-Specific Neurons for Steering LLMs in Text Style TransferCode1
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP ModelsCode1
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document SummarizationCode1
FS6D: Few-Shot 6D Pose Estimation of Novel ObjectsCode1
Few-shot Image Generation with Mixup-based Distance LearningCode1
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion ModelsCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
Intentional Computational Level DesignCode0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
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