Oct 13, 2016 - Neural networks have drawn significant interest from the machine learning ... to show that for a given upper bound on the approx- imation error, shallow ... LG] 13 Oct 2016 ... ically, we aim to answer the following two questions. Give
pre-trained deep neural networks were employed, including. AlexNet and ... node detection on CT, brain segmentation, and assessing dia- betic retinopathy ...
an iterative solution, which we accomplish using a deep neural network (DNN). ..... [6] P. C. Hansen, J. G. Nagy, and D. P. O'leary, Deblurring Images: Matrices, ...
We trained a large, deep convolutional neural network to classify the 1.2 million
... neural network, which has 60 million parameters and 650,000 neurons, ...
Jan 22, 2015 - model for classification, whose classification performance is competitive to ..... All the programs are implemented using Python language .... Figure 8: Classification accuracies versus the training time for experimental data sets.
Oct 18, 2016 - arXiv preprint arXiv:1510.00149, 2015. [9] Misha Denil, Babak Shakibi, Laurent Dinh, Marc' Aurelio Ranzato, and Nando de Freitas. Predicting.
Dec 8, 2013 - class-agnostic bounding boxes along with a single score for ... VOC2007 and ILSVRC2012, while using only the top few ..... In Computer.
Mar 7, 2016 - fields of automotive, surveillance and robotics. Despite the ..... context of pedestrian detection, annotated training dataset hav- ing that ...
gorithm are widely used in solving various classification and pre- ... an overview of neural network time series prediction and existing applications of deep ...
A denial-of-service attack (DoS attack) is typically accomplished by flooding the targeted ... TCP SYN attacks : This type of attacks exploits a flaw in some imple-.
neural networks based on local convolution filters to predict the underlying unknown non-linear ... situations where a structural relation between input and output is presumably present but unknown, when ... 2016) to natural language .... generation
Jul 13, 2018 - Deep generative neural networks for novelty generation: ...... functions. This means that it is possible to use stochastic gradient descent (SGD).
Geoffrey Hinton. University of Toronto. Canada. Paper with same name to ... Image. Convolutional layer: convolves its in
with recently-proposed batch-mode RL algorithms (for learning policies). An emphasis is put on .... gradients as standar
Online|ebook pdf|AUDIO. Book details ... Learning and Neural Networks {Free Online|ebook ... descent, cross-entropy, reg
Jan 14, 2016 - For example, Locally Linear Embedding (Roweis & Saul, ..... Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y. Read-.
classification to concepts not used during training: EMNIST/Omniglot letters ... incorrectly classifies the input with high confidence (termed adversarial exam- ..... law underlying the EMNIST ensemble of data and obtain results with statistic .....
leviated using a patch-ââbased approach (Li and Wand, 2016a) similar to ... er with adversarial losses1 (Goodfellow et al., 2014), another recent and very ...
Apr 22, 2018 - leaky integrated-and-fire (LIF) neuron (the mostly used neuron model) [49]. ... A common formula for the STDP rule is given in Eq. 1. âw = (. Ae.
lying visual object recognition in the human brain. Results. Construction of a deep neural network performing at human level in object categorization. To.
Dec 5, 2016 - recent examples in cyber security and autonomous vehicles. Despite the ... investigate this issue shared across previous research work and to propose a ...... examples taken from the DREBIN Android malware dataset.
intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed ...
Discrete Cosine Transform. DER. Diarisation Error Rate. DHC ...... A Variational Bayes (VB) system is presented by Kenny et al. (2010) which aimed to bring.
... to train deep architectures. 10. Page 11. 11. Slide from: https://deeplearningworkshopnips2010.files.wordpress.com/2010/09/nips10-workshop-tutorial-final.pdf ...
Introduction to Deep Neural Networks Dr. Asifullah Khan, DCIS, PIEAS, Pakistan
Outlines • Journey from shallow to Deep learning • Shortcomings of BPNN • Details of Deep NN • • • •
RBM DBN Auto Encoders CNN
Single Layer Perceptron for Pattern Classification Architecture
Thus the Neuron fires if net b xi wi wT x b 0 i
wT x b
Discrimination Hyper plane
Thus –b can be thought of as a threshold which when exceeded would cause the neuron to fire
5
6
Back Propogation Advantages Multi layer networks trained by back propogation algorithm allow any mapping between input and output What is wrong with back propogation? Requires labeled training data Almost all data is unlabeled Learning time does not scale well Very slow with multiple hidden layers Vanishing gradients Overfitting In 90’s,one of the important reasons of Backpropagators not providing satisfactory results on complicated problems was that hardware for processing was not that advanced as it is today.
Positive Phase • Input sample ‘v’ given to input layer • ‘v’ is feedforwarded to hiddenlayer. The result of hidden layer activations is ‘h’ Negative Phase • Propogate ‘h’ back to visible layer with result ‘v`’ • Propogate new ‘v`’ back to hidden layer with activations result ‘h`’ Weight update w(t+1) = w(t) + α(vhT – v`h`T) 27
Summary: Deep Neural Networks….cont. DNN have both Generative and Discriminative abilities Offer good Generalization; Unsupervised Pre-training
DNN have capability of Dynamic Feature Extraction Exploitation of Hardware resources for Parallel Processing (GPU, etc.,) ( Matrix Multiplication, Exploiting “No Data-Dependency”) 42
Thank You 43
References [1] Bengio, Yoshua, et al. "Greedy layer-wise training of deep networks."Advances in neural information processing systems 19 (2007): 153. [2] Larochelle, Hugo, et al. "Exploring strategies for training deep neural networks." The Journal of Machine Learning Research 10 (2009): 1-40. [3] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554. [4] Vincent, Pascal, et al. "Extracting and composing robust features with denoising autoencoders." Proceedings of the 25th international conference on Machine learning. ACM, 2008. [5] Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends® in Machine Learning 2.1 (2009): 1-127. [6] Erhan, Dumitru, et al. "The difficulty of training deep architectures and the effect of unsupervised pre-training." International Conference on artificial intelligence and statistics. 2009. 44
