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Dynamic Memory Networks for Visual and Textual Question Answering
Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training
New Guide on Creating an App & Making Money | Bilue
Experience Design Mobile Apps Cloud Engineering Enterprise Integrations Web Development Smart TV Contact Us Why Mobile Apps?If you are reading this, then you have most likely heard of the app craze that has taken over in recent years. A record 218 billion apps were downloaded last year while consumer spending leaped 20% to hit $143 billion, for the first time. The need to create and make money wi
Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a s
Q&A: Andrew Ng, chief scientist, Chinese search giant Baidu
Artificial intelligence whiz Andrew Ng hangs his hat these days at a nondescript building in Sunnyvale that serves as the Silicon Valley outpost of the Chinese search giant Baidu. The modest digs belie Baidu’s big Asian footprint. With more than 600 million monthly active mobile users, it’s often referred to as “the Google of China.” Like Google, Baidu has been exploring artificial intelligence fo
Yahoo open-sources CaffeOnSpark deep learning framework for Hadoop
Yahoo open-sources CaffeOnSpark deep learning framework for Hadoop Yahoo today is releasing some key artificial intelligence software (AI) under an open-source license. The company last year built a library called CaffeOnSpark to perform a popular type of AI called “deep learning” on the vast swaths of data kept in its Hadoop open-source file system for storing big data. Now it’s becoming availabl
なぜcuDNNのConvolutionは高速なのか | 射撃しつつ前転
cuDNNはNVIDIAが公開しているDeep Learning用のライブラリである。このライブラリを使うとCaffeやChainerなどのDeep Learning用のソフトウェアの速度が向上する。 この速度向上に寄与している最も大きな部分がConvolutionの高速化である。 個人的には、CPUでどこまで高速にConvolutionが実現できるのかに興味がある。本記事は、その準備段階として、どういう高速化戦略がありえるのかを調べたものである。 ConvolutionとはConvolutionは、日本語では畳み込みと呼ばれる操作である。畳み込み操作自体は何次元のデータ構造に対しても定義できるが、以下では、画像処理でよく使われる、二次元のConvolutionのみを考える。 何も考えずに普通にConvolutionを実装すると、以下の擬似コードのようになるだろう。ただし、簡単のため、境界
All you need is a good init
Layer-sequential unit-variance (LSUV) initialization - a simple method for weight initialization for deep net learning - is proposed. The method consists of the two steps. First, pre-initialize weights of each convolution or inner-product layer with orthonormal matrices. Second, proceed from the first to the final layer, normalizing the variance of the output of each layer to be equal to one. Expe
[Survey]Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 - Qiita
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio BinaryNetは、weightとactivationを2値化したhardware friendlyな**DNN(Deep Neural Network)**です。 論文ではactivationと書かれていますが、たぶん活性化関数の出力のことかなと思います。 要は重みにかけられる方の値です。 これにより以下の特徴が得られます。 ・１bitなのでメモリを劇的に減らせる ・掛け算器をXNORに置き換えられる（回路規模、処理
![[Survey]Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 - Qiita](https://cdn-ak-scissors.b.st-hatena.com/image/square/48a0ffde6db922b87641f48eb4643e97e91203f1/height=288;version=1;width=512/https%3A%2F%2Fqiita-user-contents.imgix.net%2Fhttps%253A%252F%252Fqiita-user-contents.imgix.net%252Fhttps%25253A%25252F%25252Fcdn.qiita.com%25252Fassets%25252Fpublic%25252Farticle-ogp-background-afbab5eb44e0b055cce1258705637a91.png%253Fixlib%253Drb-4.0.0%2526w%253D1200%2526blend64%253DaHR0cHM6Ly9xaWl0YS11c2VyLXByb2ZpbGUtaW1hZ2VzLmltZ2l4Lm5ldC9odHRwcyUzQSUyRiUyRnFpaXRhLWltYWdlLXN0b3JlLnMzLmFtYXpvbmF3cy5jb20lMkYwJTJGMTAwNTIzJTJGcHJvZmlsZS1pbWFnZXMlMkYxNDczNzA4MTIwP2l4bGliPXJiLTQuMC4wJmFyPTElM0ExJmZpdD1jcm9wJm1hc2s9ZWxsaXBzZSZmbT1wbmczMiZzPTlmNjVlN2Y3NTE0MGQwZWNmYjAwNzhkMmM3ZDJmMTZj%2526blend-x%253D120%2526blend-y%253D467%2526blend-w%253D82%2526blend-h%253D82%2526blend-mode%253Dnormal%2526s%253D1c718de2b30c00590130a4e7c1781925%3Fixlib%3Drb-4.0.0%26w%3D1200%26fm%3Djpg%26mark64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTk2MCZoPTMyNCZ0eHQ9JTVCU3VydmV5JTVEQmluYXJpemVkJTIwTmV1cmFsJTIwTmV0d29ya3MlM0ElMjBUcmFpbmluZyUyMERlZXAlMjBOZXVyYWwlMjBOZXR3b3JrcyUyMHdpdGglMjBXZWlnaHRzJTIwYW5kJTIwQWN0aXYlRTIlODAlQTYmdHh0LWFsaWduPWxlZnQlMkN0b3AmdHh0LWNvbG9yPSUyMzFFMjEyMSZ0eHQtZm9udD1IaXJhZ2lubyUyMFNhbnMlMjBXNiZ0eHQtc2l6ZT01NiZ0eHQtcGFkPTAmcz05MDFkNTZkNDk3OTBkODE1YThlOTJjYjg2MmM0NjdlMw%26mark-x%3D120%26mark-y%3D112%26blend64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTgzOCZoPTU4JnR4dD0lNDBzdXBlcnNhaWFrdWppbiZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTM2JnR4dC1wYWQ9MCZzPTllZGIzOTQ1YThjNDUxMjIwMWZhYTU3YjM2MTYwODI5%26blend-x%3D242%26blend-y%3D480%26blend-w%3D838%26blend-h%3D46%26blend-fit%3Dcrop%26blend-crop%3Dleft%252Cbottom%26blend-mode%3Dnormal%26s%3D5df87237f12a9c671432e8ae8c8f7ad3)
EIE: Efficient Inference Engine on Compressed Deep Neural Network
State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the require
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entir
Convolutional Rectifier Networks as Generalized Tensor Decompositions
Convolutional rectifier networks, i.e. convolutional neural networks with rectified linear activation and max or average pooling, are the cornerstone of modern deep learning. However, despite their wide use and success, our theoretical understanding of the expressive properties that drive these networks is partial at best. On the other hand, we have a much firmer grasp of these issues in the world
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge. Our method combines fictitious se
Exploring the Neural Algorithm of Artistic Style
We explore the method of style transfer presented in the article "A Neural Algorithm of Artistic Style" by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge (arXiv:1508.06576). We first demonstrate the power of the suggested style space on a few examples. We then vary different hyper-parameters and program properties that were not discussed in the original paper, among which are the recognitio
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performanc
Why are deep nets reversible: A simple theory, with implications for training
Generative models for deep learning are promising both to improve understanding of the model, and yield training methods requiring fewer labeled samples. Recent works use generative model approaches to produce the deep net's input given the value of a hidden layer several levels above. However, there is no accompanying "proof of correctness" for the generative model, showing that the feedforward d
みずほリサーチ&テクノロジーズ ： 人工知能の応用へ向けた筋道を探る（1/2）
人工知能に対する研究開発が活発化し、音声認識や画像認識をはじめとしていくつかの実用化事例も散見される様になっている。しかし、現状で実用化されている範囲は、人工知能の最終的な形態（＝人間と同程度以上の知能）のうちの一部分である。本論では、人工知能の現状において何が達成されていて何が未達成なのか、未達成の部分を実現するためにクリアすべき課題は何かについて整理するとともに、今後それらの課題が解決され実用化されていく筋道について、試論を提出する。 人工知能の応用へ向けた筋道を探る(PDF/1,425KB) 人工知能の研究開発に対する投資が世界的に加速している。Google、Microsoft、FacebookなどのIT企業はもとより、トヨタ自動車の米国での研究センターの設立1など、大きな注目を集めている。日本政府による「日本再興戦略」においても、IoT、ビッグデータと並び人工知能による今後の産業構
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GitHub - icoxfog417/rnn_demo: Recurrent Neural Network Demo by PyBrain
GitHub - yahoo/CaffeOnSpark: Distributed deep learning on Hadoop and Spark clusters.
