Learning Machines

Learning Machines

Universal Learning Machines

All existing learning methods have particular bias that makes them suitable for specific kind of problems. Universal Learning Machine (ULM) should find the simplest data model for arbitrary data distributions. Several ways to create ULMs are outlined, and an algorithm based on creation of new global and local features combined with meta-learning is introduced. This algorithm is able to find sim...

Hybrid learning machines

The concept of machine intelligence (MI) is complex, and thus many theories and definitions have emerged recently. Last few decades have seen a new era of machine intelligence focusing on the principles, theoretical aspects, and design methodology of algorithms gleaned from nature and biology. Examples are artificial neural networks inspired by mammalian neural systems, evolutionary computation...

Learning Deep Inference Machines

Introduction. The traditional approach to structured prediction problems is to craft a graphical model structure, learn parameters for the model, and perform inference using an efficient– and usually approximate– inference approach, including, e.g., graph cut methods, belief propagation, and variational methods. Unfortunately, while remarkably powerful methods for inference have been developed ...

Learning and Vision Machines

The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper, we review our approach to the problem of visual perception based on supervised learning. After a brief presentation of the theoretical background, we focus on some of the engineering applications of statistical learning to computer vision and discuss the main open ...