3.1節 はじめに
タイトル | 著者名 | 年 | リンク | 登場順 |
---|---|---|---|---|
A Logical Calculus of the Ideas Immanent in Nervous Activity | McCulloch and Pitts | 1943 | 1 | |
The perceptron: A probabilistic model for information storage and organization in the brain | Rosenblatt | 1958 | PDF |
2 |
Learning representations by back-propagating errors | Rumelhart et al | 1986 | リンク | 3 |
Modèles connexionnistes de l'apprentissage | Le Cun and Fogelman-Soulié | 1987 | リンク | 4 |
The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting | Werbos | 1994 | amazon | 5 |
ImageNet Classification with Deep Convolutional Neural Networks | Krizhevsky et al | 2012 | リンク | 6 |
Deep Residual Learning for Image Recognition | He et al | 2016 | arxiv | 7 |
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine | Dahl et al | 2010 | リンク | 8 |
Binary Coding of Speech Spectrograms Using a Deep Auto-encoder | Deng et al | 2010 | リンク | 9 |
Conversational Speech Transcription Using Context-Dependent Deep Neural Networks | Seide et al | 2011 | リンク | 10 |
Long Short-Term Memory | Hochreiter and Schmidhuber | 1997 | 11 | |
Sequence to Sequence Learning with Neural Networks | Sutskever et al | 2014 | arxiv | 12 |
Neural Machine Translation by Jointly Learning to Align and Translate | Bahdanau et al | 2014 | arxiv | 13 |
A Neural Conversational Model | Vinyals and Le | 2015 | arxiv | 14 |
Attention Is All You Need | Vaswani et al | 2017 | arxiv | 15 |
Improving Language Understanding by Generative Pre-Training | Radford et al | 2018 | リンク | 16 |
3.2節 深層順伝播型ネットワーク
タイトル | 著者名 | 年 | リンク | 登場順 |
---|---|---|---|---|
Rectifier nonlinearities improve neural network acoustic models | Maas et al | 2013 | リンク | 1 |
Activation Functions: Comparison of trends in Practice and Research for Deep Learning | Nwankpa et al | 2018 | arxiv | 2 |
3.3節 畳み込みニューラルネットワーク
タイトル | 著者名 | 年 | リンク | 登場順 |
---|---|---|---|---|
Convolution Transform | Widder and Hirschman | 2015 | amazon | 1 |
ImageNet Classification with Deep Convolutional Neural Networks | Krizhevsky et al | 2012 | リンク | 2 |
Deep Residual Learning for Image Recognition | He et al | 2016 | arxiv | 3 |
3.4節 RNN:リカレントニューラルネットワーク
タイトル | 著者名 | 年 | リンク | 登場順 |
---|---|---|---|---|
Long Short-Term Memory | Hochreiter and Schmidhuber | 1997 | 1 | |
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation | Cho et al | 2014a | arxiv | 2 |
3.5節 オートエンコーダー(自己符号化器)
タイトル | 著者名 | 年 | リンク | 登場順 |
---|---|---|---|---|
Sparse coding with an overcomplete basis set: A strategy employed by V1? | Olshausen and Field | 1997 | リンク | 1 |
Sparse autoencoder | Ng et al | 2011 | 2 |
3.6節 深層ニューラルネットワークの学習
タイトル | 著者名 | 年 | リンク | 登場順 |
---|---|---|---|---|
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization | Duchi et al | 2011 | リンク | 1 |
ADADELTA: An Adaptive Learning Rate Method | Zeiler | 2012 | arxiv | 2 |
Adam: A Method for Stochastic Optimization | Kingma and Ba | 2014 | arxiv | 3 |
Dropout: A Simple Way to Prevent Neural Networks from Overfitting | Srivastava et al | 2014 | リンク | 4 |
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift | Ioffe and Szegedy | 2015 | arxiv | 5 |
3.8節 参考文献
タイトル | 著者名 | 年 | リンク | 登場順 |
---|---|---|---|---|
Linear Algebra | Hoffman and Kunze | 2015 | amazon | 1 |
An Introduction to Probability Theory and Its Applications | Feller | 1957 | amazon | 2 |
Convex Optimization | Boyd et al | 2004 | リンク | 3 |
Linear Algebra and Optimization for Machine Learning | Aggarwal | 2020 | amazon | 4 |
Pattern Recognition and Machine Learning | Bishop | 2006 | amazon amazon amazon |
5 |
Deep Learning | Goodfellow et al | 2016 | amazon amazon |
6 |
Neural Networks and Deep Learning | Aggarwal | 2023 | amazon | 7 |
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems | Abadi et al | 2015 | arxiv | 8 |
PyTorch: An Imperative Style, High-Performance Deep Learning Library | Paszke et al | 2019 | arxiv | 9 |