The first edition of deep learning IndabaX-Morocco will be held at the Faculty of Sciences and Techniques of Mohammedia in April 29-30, 2019. The event will offer a comprehensive technical program addressing recent developments in research for Artificial intelligence. There will also be socials to facilitate networking, discussion of different career opportunities in AI, and sharing of ideas to increase participation of Moroccan researchers in the field. We invite all members of the AI community to attend the event.
- Algorithms: Active Learning; Multitask and Transfer Learning; Online Learning; Representation Learning; Semi-Supervised Learning; Similarity and Distance Learning; Sparse Coding and Dimensionality Expansion; Unsupervised Learning.
- Applications: Body Pose, Face, and Gesture Analysis; Computational Biology and Bioinformatics; Computational Social Science; Computer Vision; Image Segmentation; Information Retrieval; Natural Language Processing; Network Analysis; Object Detection; Object Recognition; Privacy, Security; Quantitative Finance and Econometrics; Recommender Systems; Robotics; Speech Recognition; Sustainability; Systems Biology; Tracking and Motion in Video;
- Deep Learning: Adversarial Networks; Attention Models; CNN Architectures; Deep Autoencoders; Generative Models; Meta-Learning; Optimization for Deep Networks; Recurrent Networks; Supervised Deep Networks; Visualization.
- Neuroscience and Cognitive Science: Auditory Perception; Brain Imaging; Brain-Computer Interfaces and Neural Prostheses; Cognitive Science; Language for Cognitive Science; Problem Solving; Reasoning; Visual Perception.
- Probabilistic Methods: Bayesian Theory; Belief Propagation; Causal Inference; Gaussian Processes; Graphical Models; Hierarchical Models.
- Reinforcement Learning: Decision and Control; Exploration; Hierarchical RL; Markov Decision Processes; Navigation; Planning.
- Theory: Computational Complexity; Game Theory and Computational Economics; Information Theory; Learning Theory; Regularization; Spaces of Functions and Kernels; Statistical Physics of Learning
We also welcome position papers (2 pages) related to these areas. Work may be previously published, completed, or ongoing. Submissions will be peer-reviewed by at least 2 reviewers in the area. The workshop will not publish proceedings. We encourage researchers in the field to submit their work.
All submissions must be in PDF format. All papers should follow Latex or word templates. Here is an example of a 2 pages research paper following these gidelines. Submissions may be up to 2 pages including all figures and tables, with an additional page for references. The submissions should be in a single column. They should be typeset using 11-point or larger fonts and should have at least 1-inch margin all around. Submissions that do not follow these guidelines risk being rejected without consideration of their merits.
Submissions must state the research problem, motivation, and technical contribution.
Each accepted paper will be presented as a poster. Each poster should be at most 36W x 48H inches or 90 x 122 cm.