Papers with Code 아카이브
Active Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 7
Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model
Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
Efficient Process Reward Model Training via Active Learning
Scholar Inbox: Personalized Paper Recommendations for Scientists
Towards Unconstrained 2D Pose Estimation of the Human Spine
The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops
Low Rank Learning for Offline Query Optimization
Optimal Bayesian Affine Estimator and Active Learning for the Wiener Model
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography
CoTAL: Human-in-the-Loop Prompt Engineering, Chain-of-Thought Reasoning, and Active Learning for Generalizable Formative Assessment Scoring
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection
Active Learning For Repairable Hardware Systems With Partial Coverage
Efficient Data Selection for Training Genomic Perturbation Models
Active Learning from Scene Embeddings for End-to-End Autonomous Driving
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation
Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning
Instance-wise Supervision-level Optimization in Active Learning
Unique Rashomon Sets for Robust Active Learning
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.