Probabilistic Binary-Mask Cocktail-Party Source Separation in a Convolutional Deep Neural Network
Separation of competing speech is a key challenge in signal processing and a feat routinely performed by the human auditory brain. A long standing benchmark of the spectrogram approach to source separation is known as the ideal binary mask. Here, we train a convolutional deep neural network, on a two-speaker cocktail party problem, to make probabilistic predictions about binary masks. Our results
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection - Shengfeng He
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection Abstract Existing computational models for salient object detection primarily rely on hand-crafted features, which are only able to capture low-level contrast information. In this paper, we learn the hierarchical contrast features by formulating salient object detection as a binary labeling problem using deep lear
Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors
In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on. The detection of fashion items can be an important first step of various e-commerce applications fo
A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language mode
Fuego 1.1 vs Convolutional Neural Network Sample Games
Fuego 1.1 vs Convolutional Neural Network - Sample Games Background: In December 2014, Christopher Clark and Amos Storkey of the University of Edinburgh published their paper "Teaching Deep Convolutional Neural Networks to Play Go" on arXiv. It led to an intense discussion on the computer-go mailing list. The network reached 87% wins against Gnu Go, and 14% wins against Fuego 1.1 with 10 seconds p
Deep Karaoke_Extracting Vocal Sounds from Musical Mixtures using a Convolutional Deep Neural Network_arxiv
Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network Andrew J.R. Simpson #1 , Gerard Roma#2 , Mark D. Plumbley#3 # Centre for Vision, Speech and Signal Processing, University of Surrey Guildford, UK 1 andrew.simpson@surrey.ac.uk 2 g.roma@surrey.ac.uk 3 m.plumbley@surrey.ac.uk Abstract—Identification and extraction of singing voice from within musical mixt
Recurrent Neural Network Training with Dark Knowledge Transfer
Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predict
A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network
In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and po
A Convolutional Neural Network for Modelling Sentences
The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature
Convolutional neural network and CIFAR-10, part 2 | Nghia Ho
Last Updated on July 3, 2013 by nghiaho12 Spent like the last 2 weeks trying to find a bug in the code that prevented it from learning. Somehow it miraculously works now but I haven’t been able to figure out why. First thing I did immediately was commit it to my private git in case I messed it up again. I’ve also ordered a new laptop to replace my non-gracefully aging Asus laptop with a Clevo/Sage
Deep Learning - Software Engineer - DICE
Frostbite is a technology and collaboration platform for diverse game franchises like Battlefield, Mass Effect, Need for Speed, Dragon Age and Plant vs. Zombies Garden Warfare, as well as games such as Star Wars: Battlefront, Mirror’s Edge: Catalyst and EA Sports Rory McIlroy PGA Tour. Thousands of game developers all over the world are using Frostbite to create games with our proprietary tools. W
Convolutional neural network and CIFAR-10 | Nghia Ho
Last Updated on June 29, 2013 by nghiaho12 I’ve been experimenting with convolutional neural networks (CNN) for the past few months or so on the CIFAR-10 dataset (object recognition). CNN have been around since the 90s but seem to be getting more attention ever since ‘deep learning’ became a hot new buzzword. Most of my time was spent learning the architecture and writing my own code so I could un
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks. The proposed network, called ReNet, replaces the ubiquitous convolution+pooling layer of the deep convolutional neural network with four recurrent neural networks that sweep horizontally and vertically in both directions across the image. We evaluate the proposed ReNet on three w
WO2014105866A1 - System and method for addressing overfitting in a neural network - Google Patents
WO2014105866A1 - System and method for addressing overfitting in a neural network - Google Patents System and method for addressing overfitting in a neural network Download PDF Info Publication number WO2014105866A1 WO2014105866A1 PCT/US2013/077608 US2013077608W WO2014105866A1 WO 2014105866 A1 WO2014105866 A1 WO 2014105866A1 US 2013077608 W US2013077608 W US 2013077608W WO 2014105866 A1 WO20141058
Twitter Sentiment Analysis Using Deep Convolutional Neural Network
In the work presented in this paper, we conduct experiments on sentiment analysis in Twitter messages by using a deep convolutional neural network. The network is trained on top of pre-trained word embeddings obtained by unsupervised learning on large text corpora. We use CNN with multiple filters with varying window sizes on top of which we add 2 fully connected layers with dropout and a softmax
DRAW: A Recurrent Neural Network For Image Generation
DRAW: A Recurrent Neural Network For Image Generation Karol Gregor KAROLG@GOOGLE.COM Ivo Danihelka DANIHELKA@GOOGLE.COM Alex Graves GRAVESA@GOOGLE.COM Danilo Jimenez Rezende DANILOR@GOOGLE.COM Daan Wierstra WIERSTRA@GOOGLE.COM Google DeepMind Abstract This paper introduces the Deep Recurrent Atten- tive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel s
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 464–469, Denver, Colorado, June 4-5, 2015. c 2015 Association for Computational Linguistics UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification Aliaksei Severyn DISI, University of Trento 38123 Povo (TN), Italy severyn@disi.unitn.it Alessandro Moschitti Qatar Computing Rese
5 deep learning startups to follow in 2016
So much has happened this year in the world of deep learning, that trendy type of artificial intelligence that entails training artificial neural networks on large data sets and then getting them to make inferences about new data. There have been technical breakthroughs, acquisitions, funding deals, open source releases in the field, and even the establishment of a nonprofit research lab backed by
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Deep Learning におけるコストを考慮した Dropout率制御に関する検証