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Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay ofmachine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that also includes the forward mode. We present a method to compute gradients based solely on the directional derivative that one can comp
Posted by Sibon Li, Jan Pfeifer and Bryan Perozzi and Douglas Yarrington Today, we are excited to release TensorFlow Graph NeuralNetworks (GNNs), a library designed to makeit easy to work with graph structured data using TensorFlow. We have used anearlier version of this library in production atGoogle in a variety of contexts (for example, spam and anomaly detection, traffic estimation, YouTub
Recurrent neuralnetworks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because theerror backpropagated in time either vanishes or explodes at an exponential rate. While a number of works attempt to mitigate this effect through gated recurrent units, well-chosen parametric constraints, and skip-connections, we develop
KDD2019のPaper一覧で気になるものがあったので紹介します。 ※記載時点でまだ論文公開、発表されておらず、こちら鮮度重視の記事です。 内容に誤りがある可能性は十分あるのでご了承ください。 DeepGBMとは データマイニングのトップカンファレンスKDD2019で発表される予定の手法です。 Guolin Ke, Zhenhui Xu, Jia Zhang, Jiang Bian, and Tie-yan Liu. "DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM
Maintained by Difan Deng and Marius Lindauer. The following list considers papers related to neural architecture search.It is by no means complete. If you miss a paper on the list, please let us know. Pleasenote that althoughNAS methods steadily improve, the quality ofempirical evaluations in this field are still lagging behind compared to other areas inmachine learning,AI and optimization.
Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding. This connectivity visualization shows how strongly previous input characters influence the current target character in an autocomplete problem. For example, in the prediction of “grammar” the GRU RNN initially uses long-term memorization but as more cha
The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware.
nico-opendataniconicoでは、学術分野における技術発展への寄与を目的として、 研究者の方を対象に各種サービスのデータを公開しています。 ニコニコ動画コメント等データセット(株)ドワンゴ及び(有)未来検索ブラジルと国立情報学研究所が協力して研究者に提供しているデータセットです。 ニコニコ動画コメント等のデータが利用可能です。 利用申請フォーム※国立情報学研究所へリンクします ニコニコ大百科データ(株)ドワンゴ及び(有)未来検索ブラジルと国立情報学研究所が協力して研究者に提供しているデータセットです。 ニコニコ大百科のデータが利用可能です。 利用申請フォーム※国立情報学研究所へリンクします Nico-IllustデータセットComicolorization: Semi-AutomaticManga ColorizationChie Furusawa*、Kazuyuki Hi
Python以外も使いたくないですか？ 特にDeepLearning界隈で． Menoh開発者の岡田です．この記事ではMenohの紹介と開発に至った動機について説明します． Menohのレポジトリ： https://github.com/pfnet-research/menoh Menoh（メノウ）は学習済みのDNNモデルをONNX形式から読み込んで動作させる推論専用のライブラリです．実装はC++で書きましたが，C言語のインターフェースを持たせて，他の言語用からもその機能を呼び出しやすくしてあります．リリース時点でC++版ラッパーとC#版ラッパー，Haskell版ラッパーがあり，Ruby版ラッパーとNodeJS版ラッパー，Java（JVM）版ラッパーが開発中です．バックエンドにはIntelの開発しているMKL-DNNを採用し，GPUが無くてもIntelCPUが使える環境で高速にモデルの
We introduce hyperbolic attentionnetworks to endow neuralnetworks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallownetworks. We extend thisline of work by imposing hyperbolic geometry on the activations of neuralnetworks
Please try again on a different browser or refresh if you change your mind :) Chris Donahue,Julian McAuley, Miller Puckette This is a demo of our WaveGAN method trained on drum sound effects (paper, code). All drum sounds are synthesized in browser by a neuralnetwork. Shortcuts: Keys 1-8 play sounds. Shift+[1-8] changes sounds. Space starts/stops sequencer.
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We first gather dense correspondences for 50K persons appearing in the COCO dataset by introducing an efficient annotation pipeline. We then use our dataset to trainCNN-based systems that deliver dense correspondence 'in the wi
Dense human pose estimationaims atmapping all human pixels of an RGB image to the 3D surface of the human body. We introduce DensePose-COCO, a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on 50K COCO images. We propose DensePose-RCNN, a variant of Mask-RCNN, to densely regress part-specific UV coordinates within every human region at multiple frames p
Recurrent neuralnetworks are nowadays successfully used in an abundance of applications,going fromtext, speech and image processing to recommender systems. Backpropagation through time is the algorithm that is commonly used to train thesenetworks on specific tasks. Many deep learning frameworks have their own implementation of training and sampling procedures for recurrent neuralnetworks, whi
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