ICAIDLML 2026 - International Conference on Artificial Intelligence, Deep Learning and Machine Learning

Hanoi, Vietnam
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Conference Details

Status
Verified ✔
Conference Organized By
Iser
Conference Venue
Hanoi, Vietnam
Contact person for the event
Conference coordinator
Contact Email ID
info@iser.co
Conference Details
The key intention of ICAIDLML is to provide opportunity for the global participants to share their ideas and experience in person with their peers expected to join from different parts on the world. In addition this gathering will help the delegates to establish research or business relations as well as to find international linkage for future collaborations in their career path. We hope that ICAIDLML outcome will lead to significant contributions to the knowledge base in these up-to-date scientific fields in scope.

Important Dates

Deadline for research paper submission 11 Feb 2026
Conference Start date 26 Feb 2026
Conference End date 27 Feb 2026

Event Agenda

On-Location Conference Schedule

Join us in person for the complete conference experience with networking opportunities.

Timing Sessions
09:00 AM – 09:30 AM Registration & Tea Coffee
9:30 AM – 10:00 AM Inaugural Speech & Conference Theme Presentation
10:00 AM - 11:45 AM Poster and Physical Presentation
11:45 AM - 12:30 PM Virtual Presentation
12:30 PM – 01:30 PM Lunch Break
01:30 PM – 02:15 PM Award Function and Closing Ceremony
On-Location Benefits

Direct networking, physical materials, live Q&A sessions, and complimentary refreshments.

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Call For Paper

Deep learning
Optimization
Large-scale optimization
Hyper-parameter optimization
Model structure optimization
Regularization
Observation-dependent regularization
Generative models as regularization: semi-supervised learning
Structured learning
Temporal models with long-term dependencies
Deep learning with multiple modalities, including vision, speech and languages

Unsupervised/generative modeling
Efficient (Bayesian) inference for deep learning
Large-scale generative modelling

Reinforcement learning
Learning representations for reinforcement learning
Deep model-based and data-efficient reinforcement learning

Artificial neural networks
Association rule learning
Automata, logic and games
Bayesian networks
Clustering
Commercial software
Commercial software with open-source editions
Complex systems
Computational complexity
Computational learning theory
Computational linguistics
Computer animation
Computer science
Computer system
Concurrent algorithms and data structures
Data mining
Decision tree learning
Deep Learning
Design and analysis of algorithms
Genetic algorithms
Inductive logic programming
Intelligent systems
Lambda calculus and types
Logic and proof
Machine learning
Models of computation
Object-oriented programming
Open-source software
Pattern recognition
Reinforcement learning
Representation learning
Similarity and metric learning
Sparse dictionary learning
Supervised learning
Support vector machines
Unsupervised learning

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