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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Relationship of the Mallat Scattering Transformation (a deep convolutional network) to causal physics and complexity.
Abstract not provided.
Robust training and initialization of deep neural networks: an adaptive basis viewpoint.
Abstract not provided.
Robust Training and Initialization of Deep Neural Networks An Adaptive Basis Viewpoint.
Abstract not provided.
Relationship of the Mallat Scattering Transformation (a deep convolutional network) to causal physics complexity and topology.
Abstract not provided.
A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"
Nigam et al. reported a genetic algorithm (GA) utilizing the SELFIES representation and also propose an adaptive, neural network-based penalty that is supposed to improve the diversity of the generated molecules. The main claims of the paper are that this GA outperforms other generative techniques (as measured by the penalized logP) and that a neural network-based adaptive penalty increases the diversity of the generated molecules. In this work, we investigated the reproducibility of their claims. Overall, we were able to reproduce comparable results using the SELFIES-based GA, but mostly by exploiting deficiencies of the (easily optimizable) fitness function (i.e., generating long, sulfur containing chains). In addition, we reproduce results showing that the discriminator can be used to bias the generation of molecules to ones that are similar to the reference set. Lastly, we attempted to quantify the evolution of the diversity, understand the influence of some hyperparameters, and propose improvements to the adaptive penalty.
APPLICATION OF DEEP LEARNING NETWORKS TO SURROGATE MODELLING OF CRUD DEPOSITION
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Using Deep Neural Networks to Predict Material Types in Conditional Point Sampling Applied to Markovian Mixture Models.
Abstract not provided.
Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study
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Robust architectures initialization and training for deep neural networks via the adaptive basis interpretation.
Abstract not provided.
Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data
In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The proposed models can transfer knowledge from the domain of known classes to a new domain where the classes are unknown. A clustering algorithm is further applied in the new domain to find potentially new classes from the pool of unlabeled data. The research problem and data for this paper originated from the Gravity Spy project which is a side project of Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). The LIGO project aims at detecting cosmic gravitational waves using huge detectors. However non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of LIGO. This is undesirable as it creates problems for the gravitational wave detection process. Gravity Spy aids in glitch identification with the purpose of understanding their origin. Since new types of glitches appear over time, one of the objective of Gravity Spy is to create new glitch classes. Towards this task, we offer a methodology in this paper to accomplish this.
Impact of Safeguards Measurement Errors on Deep Neural Networks.
Abstract not provided.
Solar Transposition Modeling via Deep Neural Networks with Sky Images.
Abstract not provided.
Quantum-based Molecular Dynamics using Deep Neural Networks and AI Hardware
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