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Active Learning
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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.
Prioritizing Informative Features and Examples for Deep Learning from Noisy Data
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem
Batch Active Learning of Reward Functions from Human Preferences
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data
Towards Efficient Active Learning in NLP via Pretrained Representations
Global Safe Sequential Learning via Efficient Knowledge Transfer
Practice Makes Perfect: Planning to Learn Skill Parameter Policies
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Mode Estimation with Partial Feedback
ELAD: Explanation-Guided Large Language Models Active Distillation
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities
Bayesian Active Learning for Censored Regression
Key Patch Proposer: Key Patches Contain Rich Information
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling
Active Preference Optimization for Sample Efficient RLHF
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning
Class-Balanced and Reinforced Active Learning on Graphs
Self-consistent Validation for Machine Learning Electronic Structure
Role-Playing Simulation Games using ChatGPT
Reinforcement Learning from Human Feedback with Active Queries
Active Few-Shot Fine-Tuning
Transductive Active Learning: Theory and Applications
Active Preference Learning for Large Language Models
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets
Safe Active Learning for Time-Series Modeling with Gaussian Processes
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