This report presents an algorithm for the solution of multiple kernel learning (MKL) problems with elastic-net constraints on the kernel weights. Please see Sun et al. (2013) and Yang et al. (2011) for a review on multiple kernel learning and its extensions. In particular Yang et al. (2011) introduced the generalized multiple kernel learning (GMKL) model where the kernel weights are subject to ...

Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. DOLCE consists of a standard recurrent neural net train...

Petri nets are suitable for modelling of discrete-event systems with highly parallel and cooperating activities. In complex man-made systems, such as manufacturing systems, the systems’ specific properties, such as conflicts, deadlocks, limited buffer sizes, and finite resource constraints can be easily represented in the Petri net model. Therefore teaching of Petri net basics is an inevitable ...

Developing general purpose algorithms for learning an accurate model of dynamical systems from example traces of the system is still a challenging research problem. Predictive State Representation (PSR) models represent the state of a dynamical system as a set of predictions about future events. Our work focuses on improving Temporal Difference Networks (TD Nets), a general class of predictive ...

Petri net faulty models are useful for reliability analysis and fault diagnosis of discrete event systems. Such models are difficult to work out as long as they must be computed according to alarm propagation. This paper deals with Petri net models synthesis and identification based on neural network approaches, with regard to event propagation and to state propagation dataset. A learning neura...

Gradient-based meta-learning has been shown to be expressive enough to approximate any learning algorithm. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the MT-net, which enables the meta-learner to learn on each layer’s activation space a subspace that the task-specific learner pe...

This report contains some remarks about the backpropagation method for neural net learning. We concentrate in particular in the study of local minima of error functions and the growth of weights during learning. Rutgers Center for Systems and Control, 1988

Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing the classification accuracy from low-level features is often disregarded. We therefore proposed a two-stream deep-learning neural network strategy, ...

This paper presents experimental results of an original approach to the Neural Network learning architecture for the control and the adaptive control of mobile robots. The basic idea is to use non-recurrent multi-layer-network and the backpropagation algorithm without desired outputs, but with a quadratic criterion which spezify the control objective. To illustrate this method, we consider an e...