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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 8426–8450 of 9051 papers

TitleStatusHype
Conceptual Mapping of Controversies—0
DivCon-NeRF: Generating Augmented Rays with Diversity and Consistency for Few-shot View Synthesis—0
Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching—0
Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM—0
Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons—0
DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation—0
Synthetic Unknown Class Learning for Learning Unknowns—0
Conceptual capacity and effective complexity of neural networks—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
Diverse 3D Human Pose Generation in Scenes based on Decoupled Structure—0
Diverse Agents for Ad-Hoc Cooperation in Hanabi—0
Conceptors: an easy introduction—0
Concept-Monitor: Understanding DNN training through individual neurons—0
Diverse and Coherent Paragraph Generation from Images—0
Diverse and Consistent Multi-view Networks for Semi-supervised Regression—0
Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning—0
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty—0
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data—0
Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric—0
Diverse and Relevant Visual Storytelling with Scene Graph Embeddings—0
Concept Drift Adaptation by Exploiting Historical Knowledge—0
Diverse Audio Captioning via Adversarial Training—0
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems—0
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation—0
Diverse Beam Search for Increased Novelty in Abstractive Summarization—0
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