Call for Papers
The proposed conference solicits original, unpublished and novel papers for research publication and presentation in research track, and industry/application papers in application track. Articles describing novel ideas and applications in all areas of Algorithm, Deep Learning and Computing System are of interest, including the following:
Algorithm
▪ Analysis of algorithms
▪ Ant colony algorithm
▪ Approximation algorithm
▪ Best and worst cases
▪ Big O notation
▪ Combinatorial search
▪ Competitive analysis
▪ Computability theory
▪ Computational complexity theory
▪ Embarrassingly parallel problem
▪ Emergent algorithm
▪ Evolutionary algorithm
▪ Fast Fourier transform
▪ Genetic algorithm
▪ Graph exploration algorithm
▪ Heuristic
▪ Hill climbing
▪ Implementation
▪ Lock-free and wait-free algorithms
▪ Monte Carlo algorithm
▪ Numerical analysis
▪ Online algorithm
▪ Polynomial time approximation scheme
▪ Problem size
▪ Pseudorandom number generator
▪ Quantum algorithm
▪ Random-restart hill climbing
▪ Randomized algorithm
▪ Running time
▪ Sorting algorithm
▪ Search algorithm
▪ Stable algorithm
▪ Super-recursive algorithm
▪ Tree search algorithm
Deep Learning
Recurrent Neural Network (RNN)
Sparse Coding
Neuro-Fuzzy Algorithms
Evolutionary Methods
Convolutional Neural Networks (CNN)
Deep Hierarchical Networks (DHN)
Dimensionality Reduction
Unsupervised Feature Learning
Deep Boltzmann Machines
Generative Adversarial Networks (GAN)
Autoencoders
Deep Belief Networks
Meta-Learning and Deep Networks
Deep Metric Learning Methods
MAP Inference in Deep Networks
Deep Reinforcement Learning
Learning Deep Generative Models
Deep Kernel Learning
Graph Representation Learning
Clustering, Classification and Regression
Classification Explainability
Active learning
Incremental learning and online learning
Agent-based learning
Manifold learning
Multi-task learning
Bayesian networks and applications
Case-based reasoning methods
Statistical models and learning
Computational learning
Evolutionary algorithms and learning
Fuzzy logic-based learning
Genetic optimization
Clustering, classification and regression
Neural network models and learning
Parallel and distributed learning
Tensor Learning
Deep and Machine Learning for Big Data Analytics:
Machine learning
Model-based reasoning
Deep Learning for Computing and Network Platforms
Recommender systems
Deep Learning for Social media and networks
Deep Learning in Computer Vision
Deep learning in speech recognition
Deep Learning in Nature Language Processing
Deep Learning in Machine Translation
Deep learning in bioinformatics
Deep Learning in Medical Image Analysis
Deep Learning in Climate Science
Deep Learning in Board Game Programs
Deep and Machine Learning for Data Mining and Knowledge
Computing & Systems
▪ Activity-based computing
▪ Ambient computing
▪ Artificial and Computational Intelligence
▪ Big data-oriented computing
▪ CAD/CASE of Software and Systems
▪ Cloud Computing
▪ Cognitive computing
▪ Computer Resource Management
▪ Computing high speed sensing data
▪ Control Systems
▪ Data intensive computing
▪ Database Systems
▪ Dependable computing
▪ Design and Management of Distributed Application Systems
▪ DNA (genetic) computing
▪ Elastic computing
▪ Embedded computing
▪ Entertainment computing
▪ Fog-computing
▪ High-performance computing
▪ Human-centered computing
▪ Image Processing Systems
▪ Indeterminist computing
▪ Intelligent computation
▪ Knowledge-Based Systems
▪ Macro- and micro-computing
▪ Mobile computing
▪ Mobile Edge Computing
▪ Modelling of Client-Server Systems
▪ Molecular computing
▪ Network Computing
▪ Neural computing
▪ Object Oriented Systems
▪ Optical computing
▪ Parallel computing
▪ Peer-to-Peer computing
▪ Quantum computing
▪ Real-Time and Distributed Computing
▪ Reflective computing
▪ Reversible computing
▪ Robotic Systems
▪ Sensor-based computing
▪ Signal Processing Systems
▪ Soft Computing
▪ Software Engineering
▪ Time-sensitive/temporal computing
▪ Trusted computing
▪ Unconventional computing
▪ Urban computing
▪ Wireless computing