Add to ORKGThis paper presents a method for using the self-organizing properties of connectionist networks of simple computing elements to discover a particular type of constraint in multidimensional data. The method performs dimensionality-reduction in a wide class of situations for which an assumption of linearity need not be made about the underlying constraint surface. We present a scheme for representing the values of continuous (scalar) variables in subsets of units. The backpropagation weight updating method for training connectionist networks is extended by the use of auxiliary pressure in order to coax hidden units into the prescribed representation for scalar-valued variables
An efficient neural network technique is presented for the solution of binary constraint satisfactio...
In this paper, we propose a nonlinear dimensionality reduction method aimed at extracting lower-dime...
We consider supervised dimension reduction (SDR) for problems with discrete inputs. Existing methods...
Connectionist representations are mappings between elements in the problem domain and vectors of act...
The paper presents a technique for generating concise neural network models of physical systems. The...
A general approach to encode and unify recursively nested feature structures in connectionist networ...
Abstract—We present a new strategy called “curvilinear com-ponent analysis ” (CCA) for dimensionalit...
Abstract. We present a modification of the bottleneck neural network for dimensionality reduction. W...
Connectionist modeis, commonly referred to as neural networks, are computing models in which large n...
A novel neural network based method for feature extraction is proposed. The method achieves dimensio...
The problem of learning using connectionist networks, in which network connection strengths are modi...
Connectionist approaches to cognitive modeling make use of large networks of simple computational u...
The chapter presents methods for efficiently representing logic formulas in connectionist networks t...
Models of psychological and cognitive phenomena that are based on connectionist processing have rece...
AbstractWe formalize a notion of loading information into connectionist networks that characterizes ...
An efficient neural network technique is presented for the solution of binary constraint satisfactio...
In this paper, we propose a nonlinear dimensionality reduction method aimed at extracting lower-dime...
We consider supervised dimension reduction (SDR) for problems with discrete inputs. Existing methods...
Connectionist representations are mappings between elements in the problem domain and vectors of act...
The paper presents a technique for generating concise neural network models of physical systems. The...
A general approach to encode and unify recursively nested feature structures in connectionist networ...
Abstract—We present a new strategy called “curvilinear com-ponent analysis ” (CCA) for dimensionalit...
Abstract. We present a modification of the bottleneck neural network for dimensionality reduction. W...
Connectionist modeis, commonly referred to as neural networks, are computing models in which large n...
A novel neural network based method for feature extraction is proposed. The method achieves dimensio...
The problem of learning using connectionist networks, in which network connection strengths are modi...
Connectionist approaches to cognitive modeling make use of large networks of simple computational u...
The chapter presents methods for efficiently representing logic formulas in connectionist networks t...
Models of psychological and cognitive phenomena that are based on connectionist processing have rece...
AbstractWe formalize a notion of loading information into connectionist networks that characterizes ...
An efficient neural network technique is presented for the solution of binary constraint satisfactio...
In this paper, we propose a nonlinear dimensionality reduction method aimed at extracting lower-dime...
We consider supervised dimension reduction (SDR) for problems with discrete inputs. Existing methods...