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Diversity
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Visual Place Recognition for Large-Scale UAV Applications
Adversarial attacks to image classification systems using evolutionary algorithms
Multi-population GAN Training: Analyzing Co-Evolutionary Algorithms
GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection
Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning
Mixture of Experts in Large Language Models
Step-wise Policy for Rare-tool Knowledge (SPaRK): Offline RL that Drives Diverse Tool Use in LLMs
Sparse Regression Codes exploit Multi-User Diversity without CSI
Turning the Tide: Repository-based Code Reflection
Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework
MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding
Prompt-Free Conditional Diffusion for Multi-object Image Augmentation
Is Diversity All You Need for Scalable Robotic Manipulation?
LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation
CTR-Guided Generative Query Suggestion in Conversational Search
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
3D Shape Generation: A Survey
OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs
Efficient Skill Discovery via Regret-Aware Optimization
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RE
A Hierarchical Deep Learning Approach for Minority Instrument Detection
From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning
Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping
PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis
Statistical Multicriteria Evaluation of LLM-Generated Text
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