A guide to the literature on learning probabilistic networks from data
W Buntine - IEEE Transactions on knowledge and data …, 1996 - ieeexplore.ieee.org
The literature review presented discusses different methods under the general rubric of
learning Bayesian networks from data, and includes some overlapping work on more …
learning Bayesian networks from data, and includes some overlapping work on more …
Rule extraction from recurrent neural networks: Ataxonomy and review
H Jacobsson - Neural Computation, 2005 - direct.mit.edu
Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the
underlying RNN, typically in the form of finite state machines, that mimic the network to a …
underlying RNN, typically in the form of finite state machines, that mimic the network to a …
[PDF][PDF] A guide to recurrent neural networks and backpropagation
M Boden - the Dallas project, 2002 - wiki.eecs.yorku.ca
This paper provides guidance to some of the concepts surrounding recurrent neural
networks. Contrary to feedforward networks, recurrent networks can be sensitive, and be …
networks. Contrary to feedforward networks, recurrent networks can be sensitive, and be …
[图书][B] A field guide to dynamical recurrent networks
Acquire the tools for understanding new architectures and algorithms of dynamical recurrent
networks (DRNs) from this valuable field guide, which documents recent forays into artificial …
networks (DRNs) from this valuable field guide, which documents recent forays into artificial …
A recurrent neural network that learns to count
Parallel distributed processing (PDP) architectures demonstrate a potentially radical
alternative to the traditional theories of language processing that are based on serial …
alternative to the traditional theories of language processing that are based on serial …
Towards a generalized theory comprising digital, neuromorphic and unconventional computing
H Jaeger - Neuromorphic Computing and Engineering, 2021 - iopscience.iop.org
The accelerating race of digital computing technologies seems to be steering towards
impasses—technological, economical and environmental—a condition that has spurred …
impasses—technological, economical and environmental—a condition that has spurred …
Learning finite state representations of recurrent policy networks
Recurrent neural networks (RNNs) are an effective representation of control policies for a
wide range of reinforcement and imitation learning problems. RNN policies, however, are …
wide range of reinforcement and imitation learning problems. RNN policies, however, are …
Interest point detector and feature descriptor survey
S Krig, S Krig - Computer Vision Metrics: Textbook Edition, 2016 - Springer
Many algorithms for computer vision rely on locating interest points, or keypoints in each
image, and calculating a feature description from the pixel region surrounding the interest …
image, and calculating a feature description from the pixel region surrounding the interest …
The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction
M Casey - Neural computation, 1996 - direct.mit.edu
Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown
that an RNN performing a finite state computation must organize its state space to mimic the …
that an RNN performing a finite state computation must organize its state space to mimic the …
Markovian architectural bias of recurrent neural networks
P Tino, M Cernansky… - IEEE Transactions on …, 2004 - ieeexplore.ieee.org
In this paper, we elaborate upon the claim that clustering in the recurrent layer of recurrent
neural networks (RNNs) reflects meaningful information processing states even prior to …
neural networks (RNNs) reflects meaningful information processing states even prior to …