# AFMAML — International Conference on Advances in Foundation Models and Applied Machine Learning

- **Submission deadline**: 2026-12-20
- **Notification date**: 2027-01-20
- **Conference date**: 2027-03-26
- **Location**: Guangzhou, China
- **Trackers**: 0
- **Attendees**: 0
- **Canonical page**: https://www.myhuiban.com/conference/5980

## Call for papers

AFMAML 2027 welcomes relevant paper submissions from researchers in academia, industry, and government, such as students, engineers, practitioners, scientists, and policy makers. We welcome paper submissions with original technical and scientific research results in relevant topics. Main Topics of Interest: Track 1: Foundation Model Architectures and Theories Novel neural network architectures for foundation models (transformers, state-space models, diffusion models, etc.) Scaling laws and emergent behaviors in large-scale models Theoretical foundations of pre-training and representation learning Architectural innovations for efficiency, interpretability, and generalization Multi-modal foundation model architectures (vision-language, audio-visual, etc.) Sparse models, mixture-of-experts, and modular architectures Foundational model design for scientific computing and domain-specific applications Track 2: Training, Fine-Tuning, and Adaptation Methods Large-scale pre-training algorithms and distributed training strategies Supervised fine-tuning, instruction tuning, and model alignment Parameter-efficient fine-tuning (LoRA, adapters, prefix-tuning, etc.) Reinforcement learning from human feedback (RLHF) and AI feedback (RLAIF) Transfer learning, few-shot learning, and meta-learning Continual learning and adaptation to dynamic environments Model compression, distillation, and quantization for efficient deployment Track 3: Applied Machine Learning with Foundation Models Large language models (LLMs) for natural language processing and generation Multi-modal learning and cross-modal understanding Retrieval-augmented generation (RAG) and knowledge-enhanced models Agentic AI and autonomous systems powered by foundation models Foundation models for computer vision, speech, and audio processing Generative AI: diffusion models, GANs, VAEs, and beyond Foundation models for structured and unstructured data analytics Domain-specific foundation models (healthcare, finance, law, education, etc.) Track 4: Trustworthy, Efficient, and Scalable Foundation Models Explainability, interpretability, and transparency in foundation models Fairness, bias detection, and mitigation in large-scale AI systems Privacy-preserving machine learning and federated learning Robustness, adversarial defense, and model safety Energy-efficient training and inference for sustainable AI Benchmarking, evaluation, and responsible deployment of foundation models Regulatory compliance and ethical AI governance Track 5: Emerging Applications and Interdisciplinary Innovations Foundation models for scientific discovery (physics, chemistry, biology, climate science) AI for healthcare, biomedical informatics, and medical imaging Autonomous driving and robotics empowered by foundation models Foundation models for cybersecurity, IoT, and edge intelligence Software engineering and code generation with foundation models AI for social good, education, and creative industries Human-AI collaboration and interactive AI systems

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Source: Conference Partner — https://www.myhuiban.com/conference/5980 (rankings reproduced from CCF / ICORE / QUALIS; data cached up to 1 hour)