2,811 research outputs found
Application of artificial neural networks in nonlinear analysis of trusses
A method is developed to incorporate neural network model based upon the Backpropagation algorithm for material response into nonlinear elastic truss analysis using the initial stiffness method. Different network configurations are developed to assess the accuracy of neural network modeling of nonlinear material response. In addition to this, a scheme based upon linear interpolation for material data, is also implemented for comparison purposes. It is found that neural network approach can yield very accurate results if used with care. For the type of problems under consideration, it offers a viable alternative to other material modeling methods
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
We propose and systematically evaluate three strategies for training
dynamically-routed artificial neural networks: graphs of learned
transformations through which different input signals may take different paths.
Though some approaches have advantages over others, the resulting networks are
often qualitatively similar. We find that, in dynamically-routed networks
trained to classify images, layers and branches become specialized to process
distinct categories of images. Additionally, given a fixed computational
budget, dynamically-routed networks tend to perform better than comparable
statically-routed networks.Comment: ICML 2017. Code at https://github.com/MasonMcGill/multipath-nn Video
abstract at https://youtu.be/NHQsDaycwy
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
Space shuttle main engine fault detection using neural networks
A method for on-line Space Shuttle Main Engine (SSME) anomaly detection and fault typing using a feedback neural network is described. The method involves the computation of features representing time-variance of SSME sensor parameters, using historical test case data. The network is trained, using backpropagation, to recognize a set of fault cases. The network is then able to diagnose new fault cases correctly. An essential element of the training technique is the inclusion of randomly generated data along with the real data, in order to span the entire input space of potential non-nominal data
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
We propose a new neurally-inspired model that can learn to encode the global
relationship context of visual events across time and space and to use the
contextual information to modulate the analysis by synthesis process in a
predictive coding framework. The model learns latent contextual representations
by maximizing the predictability of visual events based on local and global
contextual information through both top-down and bottom-up processes. In
contrast to standard predictive coding models, the prediction error in this
model is used to update the contextual representation but does not alter the
feedforward input for the next layer, and is thus more consistent with
neurophysiological observations. We establish the computational feasibility of
this model by demonstrating its ability in several aspects. We show that our
model can outperform state-of-art performances of gated Boltzmann machines
(GBM) in estimation of contextual information. Our model can also interpolate
missing events or predict future events in image sequences while simultaneously
estimating contextual information. We show it achieves state-of-art
performances in terms of prediction accuracy in a variety of tasks and
possesses the ability to interpolate missing frames, a function that is lacking
in GBM
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