In this study, we tackled the reduction of computational complexity by pruning the igo game tree using the potential model based on the knowledge expression of igo. The potential model considers go stones as potentials. Specific potential distributions on the go board result from each arrangement of the stones on the go board. Pruning using the potential model categorizes the legal moves into e...

This paper addresses the problem of generalizing temporal data based on calendar (date and time) attributes. The proposed method is based on a domain generalization graph, i.e., a lattice deening a partial order that represents a set of generalization relations for the attribute. We specify the components of a domain generalization graph suited to calendar attributes. We deene granularity, subs...

Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers, often with little or no drop in classification accuracy. However, most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain classification accuracy. We start by proposing a cheap pruning algorithm for f...

The logic programming community has a love{hate relationship with operators for pruning the search space of logic programs such as cut, commit, once, conditionals and variations on these. Pruning operators typically are not declarative, result in incom-pleteness and/or unsoundness, decrease readability and exibility of code and make program analysis and transformation more diicult. Despite this...

The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns further increase this cost and may hinder real-time inference. We propose feature map and kernel pruning for reducing the computational complexity of a deep convolutional neural network. Due to coarse nature, these pruning granularities c...

The search for the optimal word sequence can be performed efficiently even in a speech recognizer with a very large vocabulary and complex models. This is achieved using pruning methods with empirically chosen parameters and the willingness to accept a certain amount of pruning errors. Quite unsatisfying though, it is state-of-the-art that such pruning errors are not directly detected but, inst...

Climate change is known as one of the most important environmental challenges. Sequestration of carbon in terrestrial ecosystems is a low-cost option that may be available in the near-term to mitigate increasing atmospheric CO2 concentrations, while providing additional benefits. In this study, we estimated the effects of planting density and grazing intensity on the potential of Atriplex canes...

Node pruning is a commonly used technique for solution acceleration in a dynamic programming network. In pruning, nodes are adaptively removed from the dynamic programming network when they are determined to not lie on an optimal path. We introduce an ε-pruning condition that extends pruning to include a possible error in the pruning step. This results in a greater reduction of the computation ...

Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integ...

Axon pruning and synapse elimination promote neural connectivity and synaptic plasticity. Stereotyped pruning of axons that originate in the hippocampal dentate gyrus (DG) and extend along the infrapyramidal tract (IPT) occurs during postnatal murine development by neurite retraction and resembles axon repulsion. The chemorepellent Sema3F is required for IPT axon pruning, dendritic spine remode...