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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 8351–8375 of 9051 papers

TitleStatusHype
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections—0
Syntactic representation learning for neural network based TTS with syntactic parse tree traversal—0
Evaluations at Work: Measuring the Capabilities of GenAI in Use—0
Condition-Transforming Variational AutoEncoder for Conversation Response Generation—0
Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation—0
Disagreement amongst counterfactual explanations: How transparency can be deceptive—0
Syntax-driven Iterative Expansion Language Models for Controllable Text Generation—0
Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments—0
Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation—0
Advancing Urban Renewal: An Automated Approach to Generating Historical Arcade Facades with Stable Diffusion Models—0
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration—0
Discovering Diverse Athletic Jumping Strategies—0
Discovering Diverse Nearly Optimal Policies with Successor Features—0
Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models—0
Discovering Evolutionary Stepping Stones through Behavior Domination—0
Discovering Generalizable Skills via Automated Generation of Diverse Tasks—0
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment—0
Discovering Patterns of Definitions and Methods from Scientific Documents—0
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality—0
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization—0
Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generation—0
Advancing the Arabic WordNet: Elevating Content Quality—0
Discovering Unsupervised Behaviours from Full-State Trajectories—0
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