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Convolutional Neural Tensor Network Architecture for Community-based Question Answering Xipeng Qiu and Xuanjing Huang Shanghai Key Laboratory of Data Science, Fudan University School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, China xpqiu@fudan.edu.cn, xjhuang@fudan.edu.cn Abstract Retrieving similar questions is very important in community-based question answering. A major
I'm trying to visualize the output of a convolutional layer in tensorflow using the function tf.image_summary. I'm already using it successfully in other instances (e. g. visualizing the input image), but have some difficulties reshaping the output here correctly. I have the following conv layer: img_size = 256 x_image = tf.reshape(x, [-1,img_size, img_size,1], "sketch_image") W_conv1 = weight_var
Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the inp
畳み込みニューラルネットワーク（CNN：Convolutional Neural Networks）とは、一体何でしょうか。 簡単に言うとニューラルネットワークの進化における一段階で、ビジョンプロセッシング、手書き文字認識、声紋分析、ロボティクスといったアプリケーションで重要な技術になりつつあります。広範囲な組み込みシステムにて利用されてる可能性が高く、組み込みシステムの最新状況に通じていたいならば、大いに関心を持つべき事柄です。 しかし、CNNは従来の信号処理ツールとは仕組みも設計方法もかなり異なります。そのため、その応用範囲の広さとは裏腹に、当面は汎用的な技術ではなく、特定用途向けのブラックボックスまたはフレームワークとして登場し、ユーザーであるデザインチームから複雑性の大部分が隠されることになりそうです。 ここでは畳み込みニューラルネットワーク（CNN：Convolutional N
この記事は、TensorFlow Advent Calendar 2016の18日目の記事です。 もともとはPredNetを実装しようと思ってConvLSTMを実装していたのですが、これ単体でも動画のフレーム予測ができるのでせっかくなので試してみようと思ってこの記事を書きました。 この前のTensorFlow UserGroupのイベント「NN論文を肴に飲む会」でも発表させていただきましたので、元となる論文の概要などが気になる方はこちらのスライドをご覧ください。 # Convolutional LSTM(畳み込みLSTM) 名前からしてどんなものなのかという想像は簡単につくと思います。従来のLSTMでは時間遷移する状態は（バッチサイズ, 中間層のユニット数）の2階テンソルでしたが、それが(バッチサイズ,縦,横,チャンネル数)の4階テンソルになったものです。その際、扱う状態が画像情報なので、
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2526–2536, Osaka, Japan, December 11-17 2016. Attention-Based Convolutional Neural Network for Semantic Relation Extraction Yatian Shen, Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science, Fudan University 825 Zhangheng Road, Shang
We design a Convolutional Neural Network (CNN) which studies correlation between discretized inverse temperature and spin configuration of 2D Ising model and show that it can find a feature of the phase transition without teaching any a priori information for it. We also define a new order parameter via the CNN and show that it provides well approximated critical inverse temperature. In addition,
Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. Many applications demand for embedded solutions that integrate into existing systems with tight real-time and power constraints. Convolutional Neural Networks (CNNs) presently achieve record-breaking accuracies in all image understanding benchmarks, but have a very hig
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1153–1162, Austin, Texas, November 1-5, 2016. c 2016 Association for Computational Linguistics Convolutional Neural Network Language Models Ngoc-Quan Pham and German Kruszewski and Gemma Boleda Center for Mind/Brain Sciences University of Trento {firstname.lastname}@unitn.it Abstract Convolutional Neural
This help only covers the parts of GLSL ES that are relevant for Shadertoy. For the complete specification please have a look at GLSL ES specification Language: Version: WebGL 2.0 Arithmetic: ( ) + - ! * / % Logical/Relatonal: ~ < > <= >= == != && || Bit Operators: & ^ | << >> Comments: // /* */ Types: void bool int uint float vec2 vec3 vec4 bvec2 bvec3 bvec4 ivec2 ivec3 ivec4 uvec2 uvec3 uvec4 ma
I am trying learn deep learning and specifically using convolutional neural networks. I'd like to apply a simple network on some audio data. Now, as far as I understand CNNs are often used for image and object recognition, and therefore when using audio people often use the spectrogram (specifically mel-spectrogram) instead of the signal in the time-domain. My question is, is it better to use an i
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolut
Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. Although DNNs have been use
Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet).
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