Analog hardware for delta-backpropagation neural networks

This is a fully parallel analog backpropagation learning processor which comprises a plurality of programmable resistive memory elements serving as synapse connections whose values can be weighted during learning with buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in a plurality of neuron layers in accordance with delta-backpropagation algorithms modified so as to control weight changes due to circuit drift

Causative factors of construction and demolition waste generation in Iraq Construction Industry

The construction industry has hurt the environment from the waste generated during construction activities. Thus, it calls for serious measures to determine the causative factors of construction waste generated. There are limited studies on factors causing construction, and demolition (C&D) waste generation, and these limited studies only focused on the quantification of construction waste. This study took the opportunity to identify the causative factors for the C&D waste generation and also to determine the risk level of each causal factor, and the most important minimization methods to avoiding generating waste. This study was carried out based on the quantitative approach. A total of 39 factors that causes construction waste generation that has been identified from the literature review were considered which were then clustered into 4 groups. Improved questionnaire surveys by 38 construction experts (consultants, contractors and clients) during the pilot study. The actual survey was conducted with a total of 380 questionnaires, received with a response rate of 83.3%. Data analysis was performed using SPSS software. Ranking analysis using the mean score approach found the five most significant causative factors which are poor site management, poor planning, lack of experience, rework and poor controlling. The result also indicated that the majority of the identified factors having a high-risk level, in addition, the better minimization method is environmental awareness. A structural model was developed based on the 4 groups of causative factors using the Partial Least Squared-Structural Equation Modelling (PLS-SEM) technique. It was found that the model fits due to the goodness of fit (GOF ≥ 0.36= 0.658, substantial). Based on the outcome of this study, 39 factors were relevant to the generation of construction and demolition waste in Iraq. These groups of factors should be avoided during construction works to reduce the waste generated. The findings of this study are helpful to authorities and stakeholders in formulating laws and regulations. Furthermore, it provides opportunities for future researchers to conduct additional research’s on the factors that contribute to construction waste generation

Biologically plausible deep learning -- but how far can we go with shallow networks?

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining success as reaching around 98% test accuracy on the MNIST data set. Here, we investigate how far we can go on digit (MNIST) and object (CIFAR10) classification with biologically plausible, local learning rules in a network with one hidden layer and a single readout layer. The hidden layer weights are either fixed (random or random Gabor filters) or trained with unsupervised methods (PCA, ICA or Sparse Coding) that can be implemented by local learning rules. The readout layer is trained with a supervised, local learning rule. We first implement these models with rate neurons. This comparison reveals, first, that unsupervised learning does not lead to better performance than fixed random projections or Gabor filters for large hidden layers. Second, networks with localized receptive fields perform significantly better than networks with all-to-all connectivity and can reach backpropagation performance on MNIST. We then implement two of the networks - fixed, localized, random & random Gabor filters in the hidden layer - with spiking leaky integrate-and-fire neurons and spike timing dependent plasticity to train the readout layer. These spiking models achieve > 98.2% test accuracy on MNIST, which is close to the performance of rate networks with one hidden layer trained with backpropagation. The performance of our shallow network models is comparable to most current biologically plausible models of deep learning. Furthermore, our results with a shallow spiking network provide an important reference and suggest the use of datasets other than MNIST for testing the performance of future models of biologically plausible deep learning.Comment: 14 pages, 4 figure

Nonparametric Weight Initialization of Neural Networks via Integral Representation

A new initialization method for hidden parameters in a neural network is proposed. Derived from the integral representation of the neural network, a nonparametric probability distribution of hidden parameters is introduced. In this proposal, hidden parameters are initialized by samples drawn from this distribution, and output parameters are fitted by ordinary linear regression. Numerical experiments show that backpropagation with proposed initialization converges faster than uniformly random initialization. Also it is shown that the proposed method achieves enough accuracy by itself without backpropagation in some cases.Comment: For ICLR2014, revised into 9 pages; revised into 12 pages (with supplements