Deep-learning-enabled generative models for plasmonic metastructures (Conference Presentation)

2020 ◽  
Author(s):  
Wenshan Cai
Keyword(s):  
Deep Learning ◽  
Generative Models

scholarly journals Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rama K. Vasudevan ◽  
Maxim Ziatdinov ◽  
Lukas Vlcek ◽  
Sergei V. Kalinin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Bayesian Methods ◽  
Deep Neural Networks ◽  
Applied Research ◽  
Modern Science ◽  
Generative Models ◽  
Knowledge Development ◽  
Physical Constraints

AbstractDeep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

scholarly journals Incorporating structural knowledge into unsupervised deep learning for two-photon imaging data

2021 ◽  
Author(s):  
Florian Eichin ◽  
Maren Hackenberg ◽  
Caroline Broichhagen ◽  
Antje Kilias ◽  
Jan Schmoranzer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Live Imaging ◽  
Temporal Changes ◽  
Generative Models ◽  
Structural Knowledge ◽  
Imaging Data ◽  
Two Photon ◽  
Latent Space ◽  
Photon Imaging ◽  
Two Photon Imaging

Live imaging techniques, such as two-photon imaging, promise novel insights into cellular activity patterns at a high spatial and temporal resolution. While current deep learning approaches typically focus on specific supervised tasks in the analysis of such data, e.g., learning a segmentation mask as a basis for subsequent signal extraction steps, we investigate how unsupervised generative deep learning can be adapted to obtain interpretable models directly at the level of the video frames. Specifically, we consider variational autoencoders for models that infer a compressed representation of the data in a low-dimensional latent space, allowing for insight into what has been learned. Based on this approach, we illustrate how structural knowledge can be incorporated into the model architecture to improve model fitting and interpretability. Besides standard convolutional neural network components, we propose an architecture for separately encoding the foreground and background of live imaging data. We exemplify the proposed approach with two-photon imaging data from hippocampal CA1 neurons in mice, where we can disentangle the neural activity of interest from the neuropil background signal. Subsequently, we illustrate how to impose smoothness constraints onto the latent space for leveraging knowledge about gradual temporal changes. As a starting point for adaptation to similar live imaging applications, we provide a Jupyter notebook with code for exploration. Taken together, our results illustrate how architecture choices for deep generative models, such as for spatial structure, foreground vs. background, and gradual temporal changes, facilitate a modeling approach that combines the flexibility of deep learning with the benefits of incorporating domain knowledge. Such a strategy is seen to enable interpretable, purely image-based models of activity signals from live imaging, such as for two-photon data.

scholarly journals Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Chemical Space ◽  
Generative Models ◽  
Inverse Design ◽  
Learning Models ◽  
Structure Modification ◽  
Design Models ◽  
Comparable Performance ◽  
And Training

Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption.

scholarly journals Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

2021 ◽  
Author(s):  
Yahia Zakaria ◽  
Mayada Hadhoud ◽  
Magda Fayek
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Sample Quality ◽  
Diversity Sampling ◽  
And Performance

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. This paper's goal is to conduct an experimental study on four recent deep learning procedural level generators for Sokoban to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models' quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

scholarly journals Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian deep learning

2021 ◽  
Vol 118 (16) ◽  
pp. e2020324118
Author(s):  
Biwei Dai ◽  
Uroš Seljak
Keyword(s):  
Dark Matter ◽  
Deep Learning ◽  
Generative Models ◽  
Generative Model ◽  
Effective Theory ◽  
High Dimensions ◽  
Wide Range ◽  
Hydrodynamical Simulations ◽  
Physical Laws ◽  
Very High

The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true data-generating process is based on physical processes, these impose symmetries and constraints, and the generative model can be created by learning an effective description of the underlying physics, which enables scaling of the generative model to very high dimensions. In this work, we propose Lagrangian deep learning (LDL) for this purpose, applying it to learn outputs of cosmological hydrodynamical simulations. The model uses layers of Lagrangian displacements of particles describing the observables to learn the effective physical laws. The displacements are modeled as the gradient of an effective potential, which explicitly satisfies the translational and rotational invariance. The total number of learned parameters is only of order 10, and they can be viewed as effective theory parameters. We combine N-body solver fast particle mesh (FastPM) with LDL and apply it to a wide range of cosmological outputs, from the dark matter to the stellar maps, gas density, and temperature. The computational cost of LDL is nearly four orders of magnitude lower than that of the full hydrodynamical simulations, yet it outperforms them at the same resolution. We achieve this with only of order 10 layers from the initial conditions to the final output, in contrast to typical cosmological simulations with thousands of time steps. This opens up the possibility of analyzing cosmological observations entirely within this framework, without the need for large dark-matter simulations.

scholarly journals Survey2Survey: A deep learning generative model approach for cross-survey image mapping

2021 ◽  
Author(s):  
Brandon Buncher ◽  
Awshesh Nath Sharma ◽  
Matias Carrasco Kind
Keyword(s):  
Deep Learning ◽  
Survey Data ◽  
Galaxy Formation ◽  
Generative Models ◽  
Physical Models ◽  
Great Promise ◽  
Reconstruction Process ◽  
Initial Application ◽  
Novel Approach ◽  
Wide Range

Abstract During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight several images in which the reconstruction process appears to have removed large artifacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.

scholarly journals Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph

2021 ◽  
Author(s):  
Baiqing Li ◽  
Hongming Chen
Keyword(s):  
Deep Learning ◽  
Prediction Models ◽  
De Novo ◽  
Quantitative Estimation ◽  
Reaction Network ◽  
Generative Models ◽  
Machine Learning Algorithms ◽  
Scoring Functions ◽  
Synthetic Accessibility ◽  
Learning Machine

<a>With the increasing application of deep learning based generative models for <i>de novo</i> molecule design, quantitative estimation of molecular synthetic accessibility becomes a crucial factor for prioritizing the structures generated from generative models. On the other hand, it is also useful for helping prioritization of hit/lead compounds and guiding retro-synthesis analysis. In current study, based on the USPTO and Pistachio reaction datasets, we created a chemical reaction network, in which a depth-first search was performed for identification of the reaction paths of product compounds. This reaction dataset was then used to build predictive model for distinguishing the organic compounds either as easy synthesize (ES) or hard-to synthesize (HS) classes. Three synthesis accessibility (SA) models were built using deep learning/machine learning algorithms. The comparison between our three SA scoring functions with other existing synthesis accessibility scoring schemes, such as SYBA, SCScore, SAScore were also carried out. and the graph based deep learning model outperforms those existing SA scores. Our results show that prediction models based on historical reaction knowledge could be a useful tool for measuring molecule complexity and estimating molecule SA.</a>

scholarly journals Envision Foundational of Convolution Neural Network

2021 ◽  
Vol 10 (6) ◽  
pp. 54-60
Author(s):  
M Venkata Krishna Reddy* ◽  
Pradeep S.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Graphics ◽  
Convolutional Neural Networks ◽  
Generative Models ◽  
Activation Functions ◽  
Machine Learning Classification ◽  
Network Intrusion ◽  
Recent Advances

1. Bilal, A. Jourabloo, M. Ye, X. Liu, and L. Ren. Do Convolutional Neural Networks Learn Class Hierarchy? IEEE Transactions on Visualization and Computer Graphics, 24(1):152–162, Jan. 2018. 2. M. Carney, B. Webster, I. Alvarado, K. Phillips, N. Howell, J. Griffith, J. Jongejan, A. Pitaru, and A. Chen. Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20. ACM, Honolulu, HI, USA, 2020. 3. A. Karpathy. CS231n Convolutional Neural Networks for Visual Recognition, 2016 4. M. Kahng, N. Thorat, D. H. Chau, F. B. Viegas, and M. Wattenberg. GANLab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation. IEEE Transactions on Visualization and Computer Graphics, 25(1):310–320, Jan. 2019. 5. J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson. Understanding Neural Networks Through Deep Visualization. In ICML Deep Learning Workshop, 2015 6. M. Kahng, P. Y. Andrews, A. Kalro, and D. H. Chau. ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models. IEEE Transactions on Visualization and Computer Graphics, 24(1):88–97, Jan. 2018. 7. https://cs231n.github.io/convolutional-networks/ 8. https://www.analyticsvidhya.com/blog/2020/02/learn-imageclassification-cnn-convolutional-neural-networks-3-datasets/ 9. https://towardsdatascience.com/understanding-cnn-convolutionalneural- network-69fd626ee7d4 10. https://medium.com/@birdortyedi_23820/deep-learning-lab-episode-2- cifar- 10-631aea84f11e 11. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen. Recent advances in convolutional neural networks. Pattern Recognition, 77:354–377, May 2018. 12. Hamid, Y., Shah, F.A. and Sugumaram, M. (2014), ―Wavelet neural network model for network intrusion detection system‖, International Journal of Information Technology, Vol. 11 No. 2, pp. 251-263 13. G Sreeram , S Pradeep, K SrinivasRao , B.Deevan Raju , Parveen Nikhat , ― Moving ridge neuronal espionage network simulation for reticulum invasion sensing‖. International Journal of Pervasive Computing and Communications.https://doi.org/10.1108/IJPCC-05- 2020-0036 14. E. Stevens, L. Antiga, and T. Viehmann. Deep Learning with PyTorch. O’Reilly Media, 2019. 15. J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson. Understanding Neural Networks Through Deep Visualization. In ICML Deep Learning Workshop, 2015. 16. Aman Dureja, Payal Pahwa, ―Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks‖, Recent Advances in Computer Science , Vol 2, Issue 3, 2019 ,PP-156-161 17. https://missinglink.ai/guides/neural-network-concepts/7-types-neuralnetwork-activation-functions-right/

scholarly journals Deep Generative Design: Integration of Topology Optimization and Generative Models

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Sangeun Oh ◽  
Yongsu Jung ◽  
Seongsin Kim ◽  
Ikjin Lee ◽  
Namwoo Kang
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Research Area ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Design ◽  
Design Data ◽  
Research Areas ◽  
Design Integration ◽  
Design Options

Abstract Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based deep generative design framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.

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