1 citations · 2 across the 9 of their papers we have counts for
9 papers
Selection in Scale-Free Small World
Zs. Palotai, Cs. Farkas, A. Lorincz
In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm…
L1 regularization is better than L2 for learning and predicting chaotic systems
Z. Szabo, A. Lorincz
Emergent behaviors are in the focus of recent research interest. It is then of considerable importance to investigate what optimizations suit the learning and prediction of chaotic…
Applying Policy Iteration for Training Recurrent Neural Networks
I. Szita, A. Lorincz
Recurrent neural networks are often used for learning time-series data. Based on a few assumptions we model this learning task as a minimization problem of a nonlinear least-square…
Intelligent encoding and economical communication in the visual stream
Andras Lorincz
The theory of computational complexity is used to underpin a recent model of neocortical sensory processing. We argue that encoding into reconstruction networks is appealing for co…
Centralized reward system gives rise to fast and efficient work sharing for intelligent Internet agents lacking direct communication
Zsolt Palotai, Sandor Mandusitz, Andras Lorincz
WWW has a scale-free structure where novel information is often difficult to locate. Moreover, Intelligent agents easily get trapped in this structure. Here a novel method is put f…
Kalman-filtering using local interactions
Barnabas Poczos, Andras Lorincz
There is a growing interest in using Kalman-filter models for brain modelling. In turn, it is of considerable importance to represent Kalman-filter in connectionist forms with loca…