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Deep learning for molecular design—a review of the state of the art
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.Expand
Applying machine learning techniques to predict the properties of energetic materials
TLDR
This work presents a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints - and concludes that the best featurizing was sum over bond counting, and the best model was kernel ridge regression. Expand
Sparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysis
TLDR
This work provides a mathematical framework that enables direct control over the influence of these two types of diversity and applies the proposed framework to the development of an effective ICA algorithm that can jointly exploit independence and sparsity. Expand
A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy
TLDR
The framework and methods presented can serve as an introduction to a new type of multivariate methods for the analysis of fNIRS signals and as a blueprint for artifact rejection in complex environments beyond the applied paradigm. Expand
Machine Learning of Energetic Material Properties
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure. Further, analysisExpand
Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization
TLDR
This work proposes a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources. Expand
Deep learning for molecular generation and optimization - a review of the state of the art
TLDR
Several important high level themes emerge, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training. Expand
An efficient multivariate generalized Gaussian distribution estimator: Application to IVA
TLDR
This paper proposes an efficient estimation technique based on the Fisher scoring (FS) and demonstrates its successful application to IVA, and quantifies the performance of MGGD parameter estimation using FS and proves the effectiveness of the new IVA algorithm using simulations. Expand
Consistent Run Selection for Independent Component Analysis: Application to Fmri Analysis
TLDR
This paper provides insight into the trade-offs between estimation accuracy and algorithmic consistency with or without deviations from the assumed model and assumptions such as the statistical independence, and proposes a new metric, cross inter-symbol interference, to quantify the consistency of an algorithm across different runs. Expand
Non-orthogonal constrained independent vector analysis: Application to data fusion
TLDR
This paper proposes a general formulation for non-orthogonal constrained IVA (C-IVA) framework that can incorporate prior information about either the sources or the mixing coefficients into the IVA cost function. Expand
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