Encoding Source Language with Convolutional Neural Network for Machine Translation
The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With differen
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network
We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity. Due to its training speed and very few tunable parameters, the method has strong potential for applications requiring frequent retraining or online training. The approach is characterized by (a) convolutional filters based on biologically inspired visual processing
Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification - Microsoft Research
Supervised learning using deep convolutional neural network has shown its promise in large-scale image classification task. As a building block, it is now well positioned to be part of a larger system that tackles real-life multimedia tasks. An unresolved issue is that such model is trained on a static snapshot of data. Instead, this paper positions the training as a continuous learning process as
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
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
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
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
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
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
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Convolutional Neural Network for Sentence Classification
The goal of a Knowledge Base–supported Question Answering (KB-supported QA) system is to answer a query natural language by obtaining the answer from a knowledge database, which stores knowledge in the form of (entity, relation, value) triples. QA systems understand questions by extracting entity and relation pairs. This thesis aims at recognizing the relation candidates inside a question. We defi
Convolutional Neural Network (CNN) for Audio
I have been following the tutorials on DeepLearning.net to learn how to implement a convolutional neural network that extracts features from images. The tutorial are well explained, easy to understand and follow. I want to extend the same CNN to extract multi-modal features from videos (images + audio) at the same time. I understand that video input is nothing but a sequence of images (pixel inten
Chainerで書いたConvolutional Neural Network。MNISTの分類タスク。 · GitHub
train_mnist_cnn.py #coding: utf-8 import numpy as np import chainer from chainer import cuda import chainer.functions as F from chainer import optimizers import time from sklearn.datasets import fetch_mldata from sklearn.cross_validation import train_test_split import pylab import matplotlib.pyplot as plt gpu_flag = 0 if gpu_flag >= 0: cuda.check_cuda_available() xp = cuda.cupy if gpu_flag >= 0 el
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