For the Javascript demo of Mixture Density Networks, here is the link. Update: A more comprehensive write-up about MDNs implemented with TensorFlow here While I was going through Grave’s paper on artificial handwriting generation, I noticed that his model is not setup to predict the next location of the pen, but trained to generate a probability distribution of what happens next to the pen, includ
Extracting and Composing Robust Features with Denoising Autoencoders Pascal Vincent vincentp@iro.umontreal.ca Hugo Larochelle larocheh@iro.umontreal.ca Yoshua Bengio bengioy@iro.umontreal.ca Pierre-Antoine Manzagol manzagop@iro.umontreal.ca Universit´e de Montr´eal, Dept. IRO, CP 6128, Succ. Centre-Ville, Montral, Qubec, H3C 3J7, Canada Abstract Previous work has shown that the difficul- ties in le
Many authors of papers I read affirm SVMs is superior technique to face their regression/classification problem, aware that they couldn't get similar results through NNs. Often the comparison states that SVMs, instead of NNs, Have a strong founding theory Reach the global optimum due to quadratic programming Have no issue for choosing a proper number of parameters Are less prone to overfitting Nee
The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon recei
Adult and covertype-1 are both binary datasets. The multiclass MNIST and TIMIT datasets are included to permit you to experiment with multiclass classification. The adult archives contain .dat files (in SVM-Light format) in addition to .mat files, but the rest only include .mat files. The adult and MNIST datasets were downloaded from Léon Bottou's LaSVM web page. The covertype-1 dataset is origina
GPUなのに学習速度があまり速くならない、あるいはCPUより遅い時ってありませんか? そういうとき自分はまず「nvidia-smi -l 1」でGPUの使用率を見て100%に近い値を維持できているかどうかチェックします。NVIDIA System Management Interfaceというものらしいです*1-lオプションに数値を指定するとn秒間隔でループしてその時のGPUの状態を出力してくれます。よく見る項目はGPU使用率、メモリ使用量、温度あたりでしょうか。 この使用率が低ければ低いほど効率的にGPU計算できていないことになります。計算以前のところがボトルネックになっている可能性が高い。 list → numpy or cupyへの変換速度で差が出る pythonのリストをchainerで使えるようにnumpy or cupyに変換する時の速度が両者でだいぶ異なるようです。後者の方が
Deep Neural Networkを使って画像を好きな画風に変換できるプログラムをChainerで実装し、公開しました。 https://github.com/mattya/chainer-gogh こんにちは、PFNリサーチャーの松元です。ブログの1行目はbotに持って行かれやすいので、3行目で挨拶してみました。 今回実装したのは”A Neural Algorithm of Artistic Style”(元論文)というアルゴリズムです。生成される画像の美しさと、画像認識のタスクで予め訓練したニューラルネットをそのまま流用できるというお手軽さから、世界中で話題になっています。このアルゴリズムの仕組みなどを説明したいと思います。 概要 2枚の画像を入力します。片方を「コンテンツ画像」、もう片方を「スタイル画像」としましょう。 このプログラムは、コンテンツ画像に書かれた物体の配置をそのま
CUDA Zone CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance
Machine Learning Group at National Taiwan University Contributors Version 2.47 released on July 9, 2023. We fix some minor bugs. Version 2.43 released on February 25, 2021. Installing the Python interface through PyPI is supported > pip install -U liblinear-official The python directory is re-organized so >>> from liblinear.liblinearutil import * instead of >>> from liblinearutil import * should b
Chih-Chung Chang and Chih-Jen Lin Version 3.33 released on July 11, 2024. We fix some minor bugs. Version 3.31 released on February 28, 2023. Probabilistic outputs for one-class SVM are now supported. Version 3.25 released on April 14, 2021. Installing the Python interface through PyPI is supported > pip install -U libsvm-official The python directory is re-organized so >>> from libsvm.svmutil imp
News 09.03.2016 MDP 3.5 released! This is a bug-fix release Note that from this release MDP is in maintenance mode. 13 years after its first public release, MDP has reached full maturity and no new features are planned in the future. If you plan to do serious machine learning in Python, use sklearn. Note though that some algorithms, notably SFA and Growing Neural Gas, are only available in MDP. We
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く