AI & Machine Learning Journal
Open Access
Double Blind
Continuous
12% acceptance rate
Peer Review
Double Blind
OA Type
Gold OA
Acceptance
12%
Time to Decision
8 weeks
Frequency
Continuous
APC
1,500 EUR
Impact Factor (2023)
7.1
CiteScore (2023)
10.4
About This Journal
The AI & Machine Learning Journal (AMLJ) is one of the most highly cited open-access venues for artificial intelligence and machine learning research. Published by Springer Academic, AMLJ is a premier platform for theoretical advances, empirical innovations, and applied breakthroughs in AI. It has a particularly strong record in deep learning, reinforcement learning, and AI safety. Reproducibility underpins every editorial decision: published code and data are a requirement, not an option.
Aims & Scope
AMLJ publishes research advancing the science and engineering of intelligent systems:
• Deep learning: architectures (transformers, diffusion models, graph neural networks), training methods, and theory
• Reinforcement learning and multi-agent systems
• Generative AI: large language models, image generation, and multimodal systems
• Probabilistic modelling: Bayesian inference, variational methods, and uncertainty quantification
• AI safety, robustness, fairness, and interpretability
• Natural language processing and speech understanding
• Computer vision and scene understanding
• AI applications in healthcare, science, and engineering — provided the methodological contribution is novel
Empirical papers must include ablation studies and strong baseline comparisons. Theoretical papers must prove all stated theorems.
• Deep learning: architectures (transformers, diffusion models, graph neural networks), training methods, and theory
• Reinforcement learning and multi-agent systems
• Generative AI: large language models, image generation, and multimodal systems
• Probabilistic modelling: Bayesian inference, variational methods, and uncertainty quantification
• AI safety, robustness, fairness, and interpretability
• Natural language processing and speech understanding
• Computer vision and scene understanding
• AI applications in healthcare, science, and engineering — provided the methodological contribution is novel
Empirical papers must include ablation studies and strong baseline comparisons. Theoretical papers must prove all stated theorems.