Detailed Simulation of Large Scale Neural Network Models

1997 ◽  
pp. 931-935 ◽  
Author(s):  
Anders Lansner ◽  
Örjan Ekeberg ◽  
Erik Fransén ◽  
Per Hammarlund ◽  
Tomas Wilhelmsson
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Network Models ◽  
Neural Network Models ◽  
Detailed Simulation

scholarly journals Evaluation of Pre-Trained Convolutional Neural Network Models for Object Recognition

2018 ◽  
Vol 7 (3.15) ◽  
pp. 95 ◽  
Author(s):  
M Zabir ◽  
N Fazira ◽  
Zaidah Ibrahim ◽  
Nurbaity Sabri
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Visual Recognition ◽  
Error Function ◽  
Network Models ◽  
Neural Network Models ◽  
California Institute Of Technology ◽  
Institute Of Technology ◽  
Similar Accuracy

This paper aims to evaluate the accuracy performance of pre-trained Convolutional Neural Network (CNN) models, namely AlexNet and GoogLeNet accompanied by one custom CNN. AlexNet and GoogLeNet have been proven for their good capabilities as these network models had entered ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and produce relatively good results. The evaluation results in this research are based on the accuracy, loss and time taken of the training and validation processes. The dataset used is Caltech101 by California Institute of Technology (Caltech) that contains 101 object categories. The result reveals that custom CNN architecture produces 91.05% accuracy whereas AlexNet and GoogLeNet achieve similar accuracy which is 99.65%. GoogLeNet consistency arrives at an early training stage and provides minimum error function compared to the other two models. 

scholarly journals Data-to-Text Generation with Content Selection and Planning

2019 ◽  
Vol 33 ◽  
pp. 6908-6915 ◽  
Author(s):  
Ratish Puduppully ◽  
Li Dong ◽  
Mirella Lapata
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Network Models ◽  
Text Generation ◽  
Neural Network Models ◽  
Generation Task ◽  
Content Selection ◽  
End To End ◽  
Two Stages

Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model1 outperforms strong baselines improving the state-of-the-art on the recently released RotoWIRE dataset.

Visualising large-scale neural network models in real-time

2012 ◽  
Author(s):  
Cameron Patterson ◽  
Francesco Galluppi ◽  
Alexander Rast ◽  
Steve Furber
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Large Scale ◽  
Network Models ◽  
Neural Network Models

scholarly journals Larger GPU-accelerated brain simulations with procedural connectivity

2020 ◽  
Author(s):  
James C Knight ◽  
Thomas Nowotny
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Network Models ◽  
Processing Unit ◽  
Spiking Neural Network ◽  
Network Simulator ◽  
Neural Network Models ◽  
Scale Modelling ◽  
Significant Step ◽  
Graphical Processing

AbstractLarge-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 × 1012 synaptic connections which, in simulations, are each typically charaterized by at least one floating-point value. This amounts to several terabytes of data – an unrealistic memory requirement for a single desktop machine. Large models are therefore typically simulated on distributed supercomputers which is costly and limits large-scale modelling to a few privileged research groups. In this work, we describe extensions to GeNN – our Graphical Processing Unit (GPU) accelerated spiking neural network simulator – that enable it to ‘procedurally’ generate connectivity and synaptic weights ‘on the go’ as spikes are triggered, instead of storing and retrieving them from memory. We find that GPUs are well-suited to this approach because of their raw computational power which, due to memory bandwidth limitations, is often under-utilised when simulating spiking neural networks. We demonstrate the value of our approach with a recent model of the Macaque visual cortex consisting of 4.13 × 106 neurons and 24.2 × 109 synapses. Using our new method, it can be simulated on a single GPU – a significant step forward in making large-scale brain modelling accessible to many more researchers. Our results match those obtained on a supercomputer and the simulation runs up to 35 % faster on a single high-end GPU than previously on over 1000 supercomputer nodes.

A Large-Scale Analysis of the Semantic Password Model and Linguistic Patterns in Passwords

2021 ◽  
Vol 24 (3) ◽  
pp. 1-21
Author(s):  
Rafael Veras ◽  
Christopher Collins ◽  
Julie Thorpe
Keyword(s):  
Neural Network ◽  
Graphical Models ◽  
Large Scale ◽  
Network Models ◽  
Neural Network Models ◽  
Part Of Speech ◽  
Large Scale Analysis ◽  
Linguistic Patterns ◽  
High Level ◽  
The Impact

In this article, we present a thorough evaluation of semantic password grammars. We report multifactorial experiments that test the impact of sample size, probability smoothing, and linguistic information on password cracking. The semantic grammars are compared with state-of-the-art probabilistic context-free grammar ( PCFG ) and neural network models, and tested in cross-validation and A vs. B scenarios. We present results that reveal the contributions of part-of-speech (syntactic) and semantic patterns, and suggest that the former are more consequential to the security of passwords. Our results show that in many cases PCFGs are still competitive models compared to their latest neural network counterparts. In addition, we show that there is little performance gain in training PCFGs with more than 1 million passwords. We present qualitative analyses of four password leaks (Mate1, 000webhost, Comcast, and RockYou) based on trained semantic grammars, and derive graphical models that capture high-level dependencies between token classes. Finally, we confirm the similarity inferences from our qualitative analysis by examining the effectiveness of grammars trained and tested on all pairs of leaks.

scholarly journals METHODS OF LARGE-SCALE SIGNALS TRANSFORMATION FOR DIAGNOSIS IN NEURAL NETWORK MODELS

2019 ◽  
Vol 0 (4) ◽  
Author(s):  
I. O. Lymariev ◽  
S. A. Subbotin ◽  
A. A. Oliinyk ◽  
I. V. Drokin
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Network Models ◽  
Neural Network Models

scholarly journals Inferring brain-wide interactions using data-constrained recurrent neural network models

2020 ◽  
Author(s):  
Matthew G. Perich ◽  
Charlotte Arlt ◽  
Sofia Soares ◽  
Megan E. Young ◽  
Clayton P. Mosher ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale ◽  
Computational Models ◽  
Network Models ◽  
Brain Regions ◽  
Neural Network Models ◽  
Neural Data ◽  
Neural Recordings ◽  
Using Data

ABSTRACTBehavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of interregion communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

scholarly journals Towards Physics-Based Generalizable Convolutional Neural Network Models for Indoor Propagation

2021 ◽  
Author(s):  
Aristeidis Seretis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Network Models ◽  
Radiowave Propagation ◽  
Indoor Propagation ◽  
Neural Network Models ◽  
Small Set ◽  
The Impact

A fundamental challenge for machine learning models for electromagnetics is their ability to predict output quantities of interest (such as fields and scattering parameters) in geometries that the model has not been trained for. Addressing this challenge is a key to fulfilling one of the most appealing promises of machine learning for computational electromagnetics: the rapid solution of problems of interest just by processing the geometry and the sources involved. The impact of such models that can "generalize" to new geometries is more profound for large-scale computations, such as those encountered in wireless propagation scenarios. We present generalizable models for indoor propagation that can predict received signal strengths within new geometries, beyond those of the training set of the model, for transmitters and receivers of multiple positions, and for new frequencies. We show that a convolutional neural network can "learn" the physics of indoor radiowave propagation from ray-tracing solutions of a small set of training geometries, so that it can eventually deal with substantially different geometries. We emphasize the role of exploiting physical insights in the training of the network, by defining input parameters and cost functions that assist the network to efficiently learn basic and complex propagation mechanisms.

scholarly journals IUCNN - deep learning approaches to approximate species' extinction risk

2021 ◽  
Author(s):  
Alexander Zizka ◽  
Tobias Andermann ◽  
Daniele Silvestro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Extinction Risk ◽  
Class Imbalance ◽  
Network Models ◽  
Training Data ◽  
Assessment Process ◽  
Learning Approaches ◽  
Neural Network Models

Aim: The global Red List (RL) from the International Union for the Conservation of Nature is the most comprehensive global quantification of extinction risk, and widely used in applied conservation as well as in biogeographic and ecological research. Yet, due to the time-consuming assessment process, the RL is biased taxonomically and geographically, which limits its application on large scales, in particular for understudied areas such as the tropics, or understudied taxa, such as most plants and invertebrates. Here we present IUCNN, an R-package implementing deep learning models to predict species RL status from publicly available geographic occurrence records (and other traits if available). Innovation: We implement a user-friendly workflow to train and validate neural network models, and subsequently use them to predict species RL status. IUCNN contains functions to address specific issues related to the RL framework, including a regression-based approach to account for the ordinal nature of RL categories and class imbalance in the training data, a Bayesian approach for improved uncertainty quantification, and a target accuracy threshold approach that limits predictions to only those species whose RL status can be predicted with high confidence. Most analyses can be run with few lines of code, without prior knowledge of neural network models. We demonstrate the use of IUCNN on an empirical dataset of ~14,000 orchid species, for which IUCNN models can predict extinction risk within minutes, while outperforming comparable methods. Main conclusions: IUCNN harnesses innovative methodology to estimate the RL status of large numbers of species. By providing estimates of the number and identity of threatened species in custom geographic or taxonomic datasets, IUCNN enables large-scale analyses on the extinction risk of species so far not well represented on the official RL.

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