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...

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...

This paper proposes a dependency treebased SRL system with proper pruning and extensive feature engineering. Official evaluation on the CoNLL 2008 shared task shows that our system achieves 76.19 in labeled macro F1 for the overall task, 84.56 in labeled attachment score for syntactic dependencies, and 67.12 in labeled F1 for semantic dependencies on combined test set, using the standalone Malt...

This paper presents a generalized technology of extraction of explicit knowledge from data. The main ideas are 1) maximal reduction of network complexity (not only removal of neurons or synapses, but removal all the unnecessary elements and signals and reduction of the complexity of elements), 2) using of adjustable and flexible pruning process (the pruning sequence shouldn't be predetermined t...

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...

Neural network pruning methods on the level of individual network parameters (e.g. connection weights) can improve generalization, as is shown in this empirical study. However, an open problem in the pruning methods known today (e.g. OBD, OBS, autoprune, epsiprune) is the selection of the number of parameters to be removed in each pruning step (pruning strength). This work presents a pruning me...

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...