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
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
Deep Learning におけるコストを考慮した Dropout率制御に関する検証
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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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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
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
Jakub Fiala
I'm an artist and creative technologist with 9 years of experience in computational arts, sound art and software development. I’m based in Berlin, and I'm currently working at Ecosia as Principal Engineer. I’m excited to talk to you about social ecology, utopian thinking, audiovisual web and visualizing data. I've recently worked on: A Tarot deck generated using machine learning A cassette EP with
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
DeepGrammar: Grammar Checking Using Deep Learning
I make a lot of dumb mistakes when I write, and I’ve always dreamed of having a smart computer that could point out the errors that escape my notice. Building such a grammar checker is hard. You can’t just write down the rules of English grammar and check that they are followed like you can when building a compiler for a programming language. Natural languages such as English have some syntactic r
IBM Watson は AlchemyAPI社 買収 で得た Deep Learning 技術 を使って、知識データベース の 拡張（学習）自動化 を実現した？ - Qiita
2015年3月16日、IBM社 が AlchemyAPI社 買収を発表した。 この買収の狙いが、Deep Learning がもつ データから 概念 や オブジェクト を自動的に抽出•識別するスキルを、Watson の知識ベース 拡張プロセス（新規取り込みデータの逐次学習による 知識＝「事実」と「ルール」の自動拡張）に取り組む ことにあるならば、これは画期的だ、と思い、買収目的について同社のプレスリリースにあたってみた。 プレスリリース（日本語訳） には、期待どおり、次の文章が書きこまれていた。 「大規模なデータ・セット内の階層をすばやく識別して関係を理解するWatsonの能力強化」 「一般的な事業や業界固有のさまざまなデータを幅広く取り込み」、「トレーニング、学習する能力」や、「絶えず進化するオントロジー（概念定義や体系化）を管理するニーズに対処できる能力といったWatsonの能力向上に
Which whale is it, anyway? Face recognition for right whales using deep learning - deepsense.ai
Right Whale Recognition was a computer vision competition organized by the NOAA Fisheries on the Kaggle.com data science platform. Our machine learning team at deepsense.ai has finished 1st! In this post we describe our solution. The challenge The goal of the competition was to recognize individual right whales in photographs taken during aerial surveys. When visualizing the scenario, do not forge
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