l [Dahl+14] Multi-task Neural Networks for QSAR Predictions, G. E.
Dalh, N. Jaitly, R. Salakhutdinov
l [Goh+ 17] Deep Learning for Computational Chemistry, G. B. Goh,
N. O. Hodas, A.Vishnu, arXiv:1701.04503
l [Romero+ 16] Diet Networks: Thin Parameters for Fat Genomics, A.
Romero, and et. al. arxiv:1611.09340
l [岡野原 16] 深層⽣生成モデルによる表現学習, IIBMP 2016
l [Lin+ 16] “Why does deep and cheap learning work so well?”, H. W.
Lin, M. Tegmark
l [Mao+ 16] Least Squares Generative Adversarial Networks, X. Mao.
And et. al. arxiv:1611.04076
l [Gomez-Bombarelli+ 17] Automatic Chemical Design using a data-
driven continous representation ofmolecules, R. Gomez-Bombarelli,
and et, al.arxiv:1610.02415
l [Tomspson+ 16] Accelerating Eulerian Fluid Simulation with
Convolutional Networks, J. Tompson, and et. al. arxiv:1607.03597
l [Altae-Tran+ 16] Low Data Drug Discovery with One-shot Learning,
H. Alatae-Tran, and et. al. arxiv:1611.03199
l [Segler+ 17] Towards “AlphaChem”: Chemical Synthesis Planning
with Tree Search and Deep Neural Network Policies, M. Segler and
et. al. arxiv:1702.00020
l [Lee+ 17] DeepTarget: End-to-end Learning Framework for
microRNA Target Prediction using Deep Recurrent Neural Networks,
B. Lee, and et.al, arxiv:1603.09123
l [Miyato+ 16] “Distributional Smoothing with Virtual Adversarial
Training”, T. Miyato, and et. al. ICLR 2016
l [Hu+ 17] Learning Discrete Representations via Information
Maximization Self Augmented Training, W. Hu and et al.