Deep Learning

Deep Structured Learning

Deep learning (also known as deep structured learning or differential programming) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.

High Quality Standards

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Leading Experts

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Complex Sollutions

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Flexible Prices

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TensorLet Team

The achievement of deep learning we did by now!

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Related Publications

[ICML Workshop] W. Bao, X.-Y. Liu, Multi-agent reinforcement learning for liquidation strategy analysis. ICML Workshop on Applications and Infrastructure for Multi-Agent Learning, 2019.
[ICML Workshop] X. Li, Y. Li, Y. Zhan, X.-Y. Liu, Optimistic Bull or Pessimistic Bear: adaptive deep reinforcement learning for stock portfolio allocation. ICML Workshop on Applications and Infrastructure for Multi-Agent Learning, 2019.
[KDD Workshop] X. Li, Y. Li, X.-Y. Liu, C. Wang, Risk management via anomaly circumvent: Mnemonic deep learning for midterm stock prediction. KDD Workshop on Anomaly Detection in Finance, 2019.
[NeurIPS Workshop] X.-Y. Liu, Z. Ding, S. Borst, A. Walid. Deep Reinforcement Learning for Intelligent Transportation Systems [PDF]. NIPS Workshop on ITS, 2018.
[NeurIPS Workshop] Z. Xiong, X.-Y. Liu, S. Zhong, H. Yang, A. Walid. Practical Deep Reinforcement Learning Approach for Stock Trading. NIPS Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, 2018.
[AAAI] F. Jiang, X.-Y. Liu, H. Lu, R. Shen. Efficient multi-dimensional tensor sparse coding using t-linear combinations. AAAI, 2018.
C. Zhu, L. Xu, X.-Y. Liu, F. Qian. Tensor-generative adversarial network with two-dimensional sparse coding: application to real-time indoor localization.IEEE International Conference on Communications (ICC), 2018.
[Geophysics] F. Qian, M. Yin, X.-Y. Liu, Y.-J. Wang, C. Lu, G.-M. Hu. Unsupervised seismic facies analysis via deep convolutional autoencoders. Geophysics, 2018.
F. Jiang, H. Li, X. Hou, B. Sheng, R. Shen, X.-Y. Liu, W. Jia, P. Li, R. Fang: Abdominal adipose tissues extraction using multi-scale deep neural network. Neurocomputing 229: 23-33, 2017.

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