9,875 research outputs found
Neural computation of arithmetic functions
A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n -bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Fast arithmetic computing with neural networks
The authors introduce a restricted model of a neuron which is more practical as a model of computation then the classical model of a neuron. The authors define a model of neural networks as a feedforward network of such neurons. Whereas any logic circuit of polynomial size (in n) that computes the product of two n-bit numbers requires unbounded delay, such computations can be done in a neural network with constant delay. The authors improve some known results by showing that the product of two n-bit numbers and sorting of n n-bit numbers can both be computed by a polynomial size neural network using only four unit delays, independent of n . Moreover, the weights of each threshold element in the neural networks require only O(log n)-bit (instead of n-bit) accuracy
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
In clinical data sets we often find static information (e.g. patient gender,
blood type, etc.) combined with sequences of data that are recorded during
multiple hospital visits (e.g. medications prescribed, tests performed, etc.).
Recurrent Neural Networks (RNNs) have proven to be very successful for
modelling sequences of data in many areas of Machine Learning. In this work we
present an approach based on RNNs, specifically designed for the clinical
domain, that combines static and dynamic information in order to predict future
events. We work with a database collected in the Charit\'{e} Hospital in Berlin
that contains complete information concerning patients that underwent a kidney
transplantation. After the transplantation three main endpoints can occur:
rejection of the kidney, loss of the kidney and death of the patient. Our goal
is to predict, based on information recorded in the Electronic Health Record of
each patient, whether any of those endpoints will occur within the next six or
twelve months after each visit to the clinic. We compared different types of
RNNs that we developed for this work, with a model based on a Feedforward
Neural Network and a Logistic Regression model. We found that the RNN that we
developed based on Gated Recurrent Units provides the best performance for this
task. We also used the same models for a second task, i.e., next event
prediction, and found that here the model based on a Feedforward Neural Network
outperformed the other models. Our hypothesis is that long-term dependencies
are not as relevant in this task
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car
We present DeepPicar, a low-cost deep neural network based autonomous car
platform. DeepPicar is a small scale replication of a real self-driving car
called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN),
which takes images from a front-facing camera as input and produces car
steering angles as output. DeepPicar uses the same network architecture---9
layers, 27 million connections and 250K parameters---and can drive itself in
real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using
DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end
deep learning based real-time control of autonomous vehicles. We also
systematically compare other contemporary embedded computing platforms using
the DeepPicar's CNN-based real-time control workload. We find that all tested
platforms, including the Pi 3, are capable of supporting the CNN-based
real-time control, from 20 Hz up to 100 Hz, depending on hardware platform.
However, we find that shared resource contention remains an important issue
that must be considered in applying CNN models on shared memory based embedded
computing platforms; we observe up to 11.6X execution time increase in the CNN
based control loop due to shared resource contention. To protect the CNN
workload, we also evaluate state-of-the-art cache partitioning and memory
bandwidth throttling techniques on the Pi 3. We find that cache partitioning is
ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201
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