[PDF] Top 20 Unconstrained Monotonic Neural Networks
Has 4598 "Unconstrained Monotonic Neural Networks" found on our website. Below are the top 20 most common "Unconstrained Monotonic Neural Networks".
Unconstrained Monotonic Neural Networks
... Abstract Monotonic neural networks have recently been proposed as a way to define in- vertible ...the Unconstrained Monotonic Neural Network (UMNN) architecture based on the ... Voir le document complet
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Structure-preserving neural networks
... The database consisted of the state vector (Eq. ( 23 )) of the 100 nodes trajectories (excluding the node at h = H , for which a no-slip condition v = 0 has been imposed). This database is split in 80 train trajectories ... Voir le document complet
17
Learning Activation Functions in Deep Neural Networks
... is monotonic, then the error surface associated with a single- layer network (no hidden layer) is guaranteed to be convex, like logistic regression (Wu, ... Voir le document complet
171
Deep neural networks for choice analysis
... its monotonic increasing property, since larger winning rates and monetary payoff leads to a higher probability of choosing the ...this monotonic increasing property is violated in the DNN model, as shown ... Voir le document complet
128
Probabilistic Robustness Estimates for Deep Neural Networks
... general rule can be given from these results. It is difficult to say which regularizer performs best. It is dataset de- pendent. The left hand side plots of Figure 1, represent- ing the validation mean absolute error, ... Voir le document complet
10
Dynamic Bayesian Networks for Integrated Neural Computation
... IV. E XAMPLE A. The Experiment A model, adapted, in terms of a DBN, from a previous work [10], illustrates our formalism. The original model used causal qualitative networks (CQN), based on interval calculus, to ... Voir le document complet
5
Assembly output codes for learning neural networks
... V. CONCLUSIONS AND PERSPECTNES A way to represent categories in multi-class problems is presented, that departs itself from the usual "grandmother cell" approach. Experimental results show that an assembly of ... Voir le document complet
6
Factorized second order methods in neural networks
... Once we have chosen a model, and supposing that this model is capable of solving a given task with a dataset of examples of this task, the main challenge is now to learn the parameters of the model from the data. Some ... Voir le document complet
84
Parallel Implementations of Hopfield Neural Networks On GPU
... ficial neural network (ANN), in hopfield model, to solve some optimization problems, since it has a highly parallel ...this neural net- ...the neural network ...hopfield networks on ... Voir le document complet
38
Spiking neural networks based on resistive memory technologies for neural data analysis
... elimination of synaptic connections which takes place mainly during early childhood and is a key mechanism for the beginning specialization of the CNS [39]. Synaptic plasticity is the ability of synapses to change their ... Voir le document complet
193
Modelling large neural networks via Hawkes processes
... real neural networks such as oscillations [ 21 , 22 ...huge neural networks as a whole, using PDE formalism [ 33 , 150 ...real networks, but once again there is no statistical proof of ... Voir le document complet
230
Utilization of neural networks to expand databases
... 2 National Research Council Canada, Morched.Zeghal@nrc.ca 1. Abstract This paper examines the potential use of the artificial neural networks (ANN) technique as a tool for expanding databases. The ... Voir le document complet
4
Approximating optimal state feedback using neural networks
... Figure 7: Data in the Center of Arcs Contributes Little to Training Symptoms of these trajectory based training examples were found both close to the origin, where [r] ... Voir le document complet
24
Adaptive motor control using predictive neural networks
... The claims made in this thesis rely mainly on empirical results obtained from simulations, and these claims need to be substantiated in tests on other dynamical s[r] ... Voir le document complet
102
Graphlet Count Estimation via Convolutional Neural Networks
... Intuitively, learning from historic graphs can make estimation more accurate and avoid many repetitive counting to reduce computational cost. Based on this idea, we propose a convolutional neural network (CNN) ... Voir le document complet
4
Back-engineering of spiking neural networks parameters
... networks with adaptive conductances. Front Comput Neurosci 2008, 2:2. doi:10.3389/neuro.10.002.2008. 2. Bohte SM, Mozer MC: Reducing the variability of neural responses: A computational theory of ... Voir le document complet
3
A RANDOM MATRIX APPROACH TO NEURAL NETWORKS
... Akhiezer and Glazman , 1993 )), this result therefore allows for the character- ization of the asymptotic spectral properties of T 1 Σ T Σ, such as its limiting spectral measure in Theorem 2 . Application-wise, the ... Voir le document complet
64
Recording and simulation of hippocampal neural networks
... [2] [3] W. Gerstner W. Kistler (2002), Spiking neurons models, Cam- bridge University Press. [4] L. Paninski, J. W. Pillow, E. P. Simoncelli (2004), Maximum likelihood estimation of a stochastic integrate-and-fire ... Voir le document complet
3
Deep neural networks for audio scene recognition
... [11] H. A. Bourlard and N. Morgan, Connectionist Speech Recognition: A Hybrid Approach, Kluwer Academic Publishers, Norwell, MA, USA, 1993. [12] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Neurocomputing: ... Voir le document complet
6
Gibbs Measures on Attractors in Biological Neural Networks
... Unit´e de recherche INRIA Lorraine, Technopˆole de Nancy-Brabois, Campus scientifique, ` NANCY 615 rue du Jardin Botanique, BP 101, 54600 VILLERS LES Unit´e de recherche INRIA Rennes, Ir[r] ... Voir le document complet
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