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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 51–75 of 9051 papers

TitleStatusHype
Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching—0
Efficient Medical VIE via Reinforcement Learning—0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Mitigating Safety Fallback in Editing-based Backdoor Injection on LLMsCode0
Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation—0
Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles—0
What Matters in Learning from Large-Scale Datasets for Robot Manipulation—0
IKDiffuser: A Generative Inverse Kinematics Solver for Multi-arm Robots via Diffusion Model—0
High Socioeconomic Status is Associated with Diverse Consumption across Brands and Price Levels—0
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing—0
Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity ControlCode0
Optimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable Task Experts—0
Data-Driven Soil Organic Carbon Sampling: Integrating Spectral Clustering with Conditioned Latin Hypercube Optimization—0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework—0
Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning—0
Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMsCode2
Neutral theory of cooperators—0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?—0
SPARKE: Scalable Prompt-Aware Diversity Guidance in Diffusion Models via RKE Score—0
UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian SplattingCode2
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language ModelsCode1
Efficient Part-level 3D Object Generation via Dual Volume PackingCode4
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)—0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments—0
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