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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 1651–1675 of 9051 papers

TitleStatusHype
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
Optimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable Task Experts—0
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing—0
Neutral theory of cooperators—0
SPARKE: Scalable Prompt-Aware Diversity Guidance in Diffusion Models via RKE Score—0
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?—0
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)—0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments—0
The Cell Ontology in the age of single-cell omicsCode0
Exploration by Random Reward Perturbation—0
Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field SamplingCode0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction—0
Diffusion Models for Safety Validation of Autonomous Driving Systems—0
Slow and Fast Neurons Cooperate in Contextual Working Memory through Timescale Diversity—0
OpenDance: Multimodal Controllable 3D Dance Generation Using Large-scale Internet Data—0
Correlated Errors in Large Language Models—0
REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models—0
MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert SpecializationCode0
A weighted quantum ensemble of homogeneous quantum classifiers—0
SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark—0
CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction—0
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
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