Machine Learning Applied
  • Machine Learning Applied
  • Categories
    • Machine Learning
    • Finance
    • Cheminformatics
    • Bioinformatics
    • Papers
  • Resources
    • Machine Learning Papers
      • Automated Machine Learning
      • Generative Adversarial Networks and Autoencoders
      • Genetics and Genomics
      • Immunology
      • Miscellaneous
      • Molecules
      • Proteins
    • Machine Learning Sites
  • About Us
Select Page

Quant And Machine Learning Links: 20230723

by GCBC Ventures | Jul 23, 2023 | Finance, Machine Learning, Papers

Quantocracy: This is a curated mashup of quantitative trading links. Reinforcement Learning for Credit Index Option Hedging – Francesco Mandelli, Marco Pinciroli, Michele Trapletti, Edoardo Vittori In this paper, we focus on finding the optimal hedging strategy...

Quant And Machine Learning Links: 20230716

by GCBC Ventures | Jul 16, 2023 | Finance, Machine Learning, Papers

Quantocracy: This is a curated mashup of quantitative trading links. Financial Machine Learning – Bryan T. Kelly, Dacheng Xiu We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line...

Paper: XPySom: High-Performance Self-Organizing Maps – Mancini et al 2020

by GCBC Ventures | Nov 20, 2020 | Machine Learning, Papers

In this paper the authors present a Python version of the Self-Organizing Map (SOM) algorithm that can run in parallel on CPUs and GPUs (via CuPy). Additionally, they speed up the algorithm by: heavily exploiting higher-dimensional operations (e.g., matrix-matrix) to...

Ebook: Deep Learning for Toxicity and Disease Prediction – Gong, Zhang, Chen (eds) 2020

by GCBC Ventures | Apr 28, 2020 | Machine Learning, Papers

Deep Learning for Toxicity and Disease Prediction is a collection of 12 articles published as a free ebook. Below is an excerpt.

Paper: Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery – Xu et al 2017

by GCBC Ventures | Apr 9, 2020 | Machine Learning, Papers

In this paper, the authors modified a seq2seq RNN constructed for language translation to a seq2seq RNN autoencoder (specifically for SMILES input-output) so that the resultant latent data space could be used as molecular fingerprints for subsequent machine learning...

Paper: CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations – Paul et al 2018

by GCBC Ventures | Apr 7, 2020 | Machine Learning, Papers

In this interesting paper, the authors use a multi input neural network to predict various small molecule properties in which one branch is a multilayer perceptron with MACCS fingerprints and the other is one of a RNN, 1D CNN, 1D CNN-RNN with SMILES. On various data...
« Older Entries
Next Entries »

Affiliate Disclaimer

At no cost to you, Machine Learning Applied earns a commission from qualified purchases when you click on the links below.

  • Disclaimer
  • Privacy Policy
  • Terms and Conditions