92,136 research outputs found
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators
The choice of activation functions and their motivation is a long-standing
issue within the neural network community. Neuronal representations within
artificial neural networks are commonly understood as logits, representing the
log-odds score of presence of features within the stimulus. We derive
logit-space operators equivalent to probabilistic Boolean logic-gates AND, OR,
and XNOR for independent probabilities. Such theories are important to
formalize more complex dendritic operations in real neurons, and these
operations can be used as activation functions within a neural network,
introducing probabilistic Boolean-logic as the core operation of the neural
network. Since these functions involve taking multiple exponents and
logarithms, they are computationally expensive and not well suited to be
directly used within neural networks. Consequently, we construct efficient
approximations named (the AND operator Approximate for
Independent Logits), , and ,
which utilize only comparison and addition operations, have well-behaved
gradients, and can be deployed as activation functions in neural networks. Like
MaxOut, and are generalizations
of ReLU to two-dimensions. While our primary aim is to formalize dendritic
computations within a logit-space probabilistic-Boolean framework, we deploy
these new activation functions, both in isolation and in conjunction to
demonstrate their effectiveness on a variety of tasks including image
classification, transfer learning, abstract reasoning, and compositional
zero-shot learning
A survey on modern trainable activation functions
In neural networks literature, there is a strong interest in identifying and
defining activation functions which can improve neural network performance. In
recent years there has been a renovated interest of the scientific community in
investigating activation functions which can be trained during the learning
process, usually referred to as "trainable", "learnable" or "adaptable"
activation functions. They appear to lead to better network performance.
Diverse and heterogeneous models of trainable activation function have been
proposed in the literature. In this paper, we present a survey of these models.
Starting from a discussion on the use of the term "activation function" in
literature, we propose a taxonomy of trainable activation functions, highlight
common and distinctive proprieties of recent and past models, and discuss main
advantages and limitations of this type of approach. We show that many of the
proposed approaches are equivalent to adding neuron layers which use fixed
(non-trainable) activation functions and some simple local rule that
constraints the corresponding weight layers.Comment: Published in "Neural Networks" journal (Elsevier
Learning Combinations of Activation Functions
In the last decade, an active area of research has been devoted to design
novel activation functions that are able to help deep neural networks to
converge, obtaining better performance. The training procedure of these
architectures usually involves optimization of the weights of their layers
only, while non-linearities are generally pre-specified and their (possible)
parameters are usually considered as hyper-parameters to be tuned manually. In
this paper, we introduce two approaches to automatically learn different
combinations of base activation functions (such as the identity function, ReLU,
and tanh) during the training phase. We present a thorough comparison of our
novel approaches with well-known architectures (such as LeNet-5, AlexNet, and
ResNet-56) on three standard datasets (Fashion-MNIST, CIFAR-10, and
ILSVRC-2012), showing substantial improvements in the overall performance, such
as an increase in the top-1 accuracy for AlexNet on ILSVRC-2012 of 3.01
percentage points.Comment: 6 pages, 3 figures. Published as a conference paper at ICPR 2018.
Code:
https://bitbucket.org/francux/learning_combinations_of_activation_function
CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"
The role of neural network modeling in the learning content of the special
course "Foundations of Mathematical Informatics" was discussed. The course was
developed for the students of technical universities - future IT-specialists
and directed to breaking the gap between theoretic computer science and it's
applied applications: software, system and computing engineering. CoCalc was
justified as a learning tool of mathematical informatics in general and neural
network modeling in particular. The elements of technique of using CoCalc at
studying topic "Neural network and pattern recognition" of the special course
"Foundations of Mathematic Informatics" are shown. The program code was
presented in a CoffeeScript language, which implements the basic components of
artificial neural network: neurons, synaptic connections, functions of
activations (tangential, sigmoid, stepped) and their derivatives, methods of
calculating the network's weights, etc. The features of the Kolmogorov-Arnold
representation theorem application were discussed for determination the
architecture of multilayer neural networks. The implementation of the
disjunctive logical element and approximation of an arbitrary function using a
three-layer neural network were given as an examples. According to the
simulation results, a conclusion was made as for the limits of the use of
constructed networks, in which they retain their adequacy. The framework topics
of individual research of the artificial neural networks is proposed.Comment: 16 pages, 3 figures, Proceedings of the 13th International Conference
on ICT in Education, Research and Industrial Applications. Integration,
Harmonization and Knowledge Transfer (ICTERI, 2018
A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery
Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.Keywords: Posterior spinal fusion surgery (PSF); Prediction, Surgical satisfaction; Multi-layer perceptron (MLP); Logistic regression (LR) (PDF) A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery. Available from: https://www.researchgate.net/publication/325679954_A_Predictive_Model_for_Assessment_of_Successful_Outcome_in_Posterior_Spinal_Fusion_Surgery [accessed Jul 11 2019].Peer reviewe
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