Deep Neural Networks for Multiple Speaker Detection and ...

26 Feb 2018 ... GCC-PHAT coefficients: The first type of input feature is represented by the ... To train such network, we adopt a two-step training scheme: First ...

Deep Neural Networks for Multiple Speaker Detection and ... - Related Documents

Deep Neural Networks for Multiple Speaker Detection and ...

26 Feb 2018 ... GCC-PHAT coefficients: The first type of input feature is represented by the ... To train such network, we adopt a two-step training scheme: First ...

Hyphenation using deep neural networks - BME TDK

27 Oct 2017 ... hyphens —nd defines ex—™tly one hyphen—tion for — wordF. Hyphenation algorithms gommonly used hyphen—tion —lgorithms —re ˜—sed ...

Deepfake Video Detection Using Recurrent Neural Networks

FakeApp is a desktop application that allows one to create what are now known as “deepfakes” videos. Deepfake videos are manipulated videoclips which were.

Random Projection in Deep Neural Networks

22 Dec 2018 ... In the first application of random projection, we make use of its original purpose,. i.e., reducing the ... Similarly to previous experiments, we test five RP constructions, i.e.,. Gaussian ... The answer to this question depends on ...

Generalisation in humans and deep neural networks

21 Dec 2018 ... generalisation abilities when tested on other distortion types. ... Since the space of possible distortions is literally unlimited (both theoretically ...

Deep Learning in Neural Networks: An Overview

8 Oct 2014 ... 1 Introduction to Deep Learning (DL) in Neural Networks (NNs). 4 ... Learning or credit assignment is about finding weights that make the NN ... 2002), because they can in principle learn to run their own weight change algo-.

Pedestrian Prediction by Planning using Deep Neural Networks

20 Jun 2017 ... Here, the intention corresponds to a spacial destination. The works of ... output evaluated in ground truth destinations as a loss function. 5 ...

Composition of Deep and Spiking Neural Networks for Very ... - arXiv

The test set contained about 36 000 syllables (syll) in. 5240 s of speech ... [57] D. B. Paul and J. M. Baker, “The design for the wall street journal- based CSR ...

Compact Deep Convolutional Neural Networks for Image ...

Abstract—Convolutional Neural Network is efficient in learn- ing hierarchical features from large datasets, but its model complexity and large memory foot prints ...

Efficient Inference on Deep Neural Networks by ... - OpenReview

reduction on ResNet-101 and ∼46% speed-up and ∼36% FLOPS reduction on ... *The work has been done as a visiting scholar at University of Waterloo.

ImageNet Classification with Deep Convolutional Neural Networks

University of Toronto [email protected] ... We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in ... Now we are ready to describe the overall architecture of our CNN. As depicted in Figure 2 ...

Artificial Neural Networks trained through Deep Reinforcement ...

18 Dec 2018 ... vortex street at moderate Reynolds number (Re = 100), our Artificial Neural ... Drag reduction and flow control are techniques of critical interest for the ... active flow control strategies is a complex endeavor (Duriez et al., 2016). ... In addition, we want to thank Pr. Thierry Coupez and Pr. Elie Hachem for ...

DeepBase: Deep Inspection of Neural Networks - arXiv

7 Jan 2019 ... Ian Huang. Columbia University ... 7drive.google.com/file/d/0B6N7tANPyVeBWE9WazRYaUd2QTg/view. 8stanfordnlp.github.io/. NNP. VBZ. RB.

Non-chord Tone Identification Using Deep Neural Networks

Yaolong Ju. Schulich School of Music, McGill University. Centre for Interdisciplinary Research in Music Media and. Technology (CIRMMT). Montréal, Canada.

Distributed Deep Neural Networks over the Cloud, the Edge and ...

(DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep ...

Deep Neural Networks for Optimal Team Composition - arXiv

8 May 2018 ... Deep Neural Networks for Optimal Team Composition. Anna Sapienza∗. USC Information Sciences Institute. Marina del Rey, CA, USA.

Conceptual Content in Deep Convolutional Neural Networks - arXiv

Networks: An analysis into multi-faceted properties of neurons. Zahra Sadeghi ... neurons in higher layers comprise single-faceted property and tend to respond ...

Deep Neural Networks for Acoustic Modeling in ... - Google Research

27 Apr 2012 ... Hinton, Dahl, Mohamed, and Jaitly are with the University of Toronto. ... The DNN-HMM training recipe developed for the Bing voice search ...

AI Benchmark: Running Deep Neural Networks on Android ... - arXiv

30 Sep 2018 ... While there exists a number of common benchmarks test- ing the CPU and GPU performance of mobile phones, none of them measure the ...

Deep Neural Networks for Acoustic Modeling in Speech Recognition

[Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed,. Navdeep Jaitly ... The DNN-HMM training recipe developed for the Bing voice.

XDeep: An Interpretation Tool for Deep Neural Networks - arXiv

4 Nov 2019 ... XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers. Overall, XDeep takes a ...

A Defence Against Trojan Attacks on Deep Neural Networks - arXiv

Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the ... However, the safety of ML system deployments has now been recognized ... (class 2), 'cat' (class 3), 'deer' (class 4), 'dog' (class 5) and 'frog' (class 6).

Competitive Bridge Bidding with Deep Neural Networks - arXiv

5 Mar 2019 ... e game uses a standard deck of 52 cards with 4 suits (club ♧, diamond ♢ ... most widely used scoring mechanism is the Duplicate Bridge Scoring ... [20] Nikolai Yakovenko, Liangliang Cao, Colin Ra el, and James Fan. 2016.

deep neural networks can detect sexual orientation from faces 1 2 ...

computational social science, big data, privacy, artificial intelligence. 49 ... Figure 2. The accuracy of the DNN-based sexual orientation classifier against the number of ... Proceedings of the National Academy of Sciences of the United States of.

Constructing Deep Neural Networks by Bayesian Network Structure ...

17 Oct 2018 ... [email protected] Gal Novik ... arXiv:1806.09141v3 [stat.ML] 17 Oct 2018 ... Cooper, Gregory F and Herskovits, Edward. A Bayesian ...

Manga FaceNet: Face Detection in Manga based on Deep Neural ...

6 Jun 2017 ... Although the book market slumped, in Japan the market of compiled manga books keep creating record-high sales and reach around 2.4 ...

Co-Evolving a Go-Playing Neural Network - Neural Networks ...

the best hosts of previous generations (i.e. a hall of fame,. Rosin, 1997). As the evolutionary technique we will use the SANE neuro-evolution method (Moriarty ...

Multiple-Well, Multiple- Path Unimolecular Reaction ... - Deep Blue

ABSTRACT: Unimolecular reaction systems in which multiple isomers undergo simultaneous reactions via multiple decomposition reactions and multiple ...

Neural Networks with R

R and wants to work with neural networks to get better results from complex data. If you are ... The neural network adjusts its own weights, so that similar.

Convolutional Neural Networks

PARRSLAB. • ConvNetJS demo: training on CIFAR-10. • http://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html. 27. CNN Demo ...

An Introduction to Neural Networks

11.4 Neural networks and intelligent systems: symbols versus neurons. 11.5 A brief ... The final chapter tries to make sense of the seemingly disparate collection ... consider the first layer of input distribution points as units in their own right, but.

Neural Metaphor Detection in Context

sensory features, bag-of-words features, WordNet features. Rei et al., 2017, Köper and im Walde, 2017, Bulat et al.,. 2017, Shutova et al., 2016, Tekiroglu et al., ...

6.867 Machine learning and neural networks

6.867 Machine learning and neural networks. Tommi Jaakkola ... Tommi Jaakkola ([email protected]). • TA: Nathan Srebro ([email protected]). • About the course:.

Getting Started With Convolutional Neural Networks

I built my first network using a ConvNetJS, which is a javascript deep learning library by Andrej Karpathy. Page 2. Dinç Çiftçı. He has a long write up on the library ...

Neural Networks: MATLAB examples

11. nn06_rbfn_xor - Radial basis function networks for classification of XOR problem. 12. nn07_som - 1D and 2D Self Organized Map. 13. nn08_tech_diag_pca ...

Lecture 10 Recurrent neural networks

When there is no separate target sequence, we can get a teaching signal by trying to predict the next term in the input sequence. – The target output sequence is ...