Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The result are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces negative weights into the RAM net is reported. The results obtained on the task of recognizing handwritten digits is comparable with the best performances r...

We study feed-forward nets with arbitrarily many layers, using the standard sigmoid, tanh x. Aside from technicalities, our theorems are: 1. Complete knowledge of the output of a neural net for arbitrary inputs uniquely specifies the architecture, weights and thresholds; and 2. There are only finitely many critical points on the error surface for a generic training problem. Neural nets were ori...

The paper describes the operation principles of the evolutive neuro fuzzy filtering (ENFF) properties, which based on back propagation fuzzy neural net, this filter adaptively choose and emit a decision according with the reference signal changes of an external reference process, in order to actualize the best correct new conditions updating a process. This neural net fuzzy filter mechanism sel...

This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning. It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure o...

In this paper we derive novel algorithms for estimation of regularization parameters and for optimization of neural net architectures based on a validation set. Regularization parameters are estimated using an iterative gradient descent scheme. Architecture optimization is performed by approximative combinatorial search among the relevant subsets of an initial neural network architecture by emp...

While neural net models have been developed to a high degree of sophistication, they have some drawbacks at a more integrative, “architectural” level of analysis. We describe a “hybrid” cognitive architecture that is implementable in neuronal nets, and which has uniform brain-like features, including activation-passing and highly distributed “codelets,” implementable as small-scale neural nets....

While neural net models have been developed to a high degree of sophistication, they have some drawbacks at a more integrative, "architectural" level of analysis. We describe a "hybrid" cognitive architecture that is implementable in neuronal nets, and which has uniform brainlike features, including activation-passing and highly distributed "codelets," implementable as small-scale neural nets. ...

Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in medical image segmentation tasks. A common feature most top-performing CNNs is an encoder-decoder architecture inspired by the U-Net. For multi-region brain tumor segmentation, 3D U-Net and its variants provide competitive performances. In this work, we propose interesting extension of standard architecture, ...