Deep Learning Enabled Inorganic Material Generator

<div>Recent years have witnessed utilization of modern machine learning approaches for predicting properties of material using available datasets. However, to identify potential candidates for material discovery, one has to systematically scan through a large chemical space and subsequently calculate the properties of all such samples. On the other hand, generative methods are capable of efficiently sampling the chemical space and can generate molecules/materials with desired properties. In this study, we report a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based upon conditional variational autoencoders (CVAE) and the predictor module consists of three deep neural networks trained for predicting enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules.</div><div><br></div>

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Diversity oriented Deep Reinforcement Learning for targeted molecule generation

Journal of Cheminformatics ◽

10.1186/s13321-021-00498-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Tiago Pereira ◽

Maryam Abbasi ◽

Bernardete Ribeiro ◽

Joel P. Arrais

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Reinforcement Learning ◽

Deep Neural Networks ◽

Chemical Space ◽

Biological Properties ◽

Training Process ◽

Training Strategy ◽

Inhibitory Power ◽

Exploratory Strategy

AbstractIn this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine $$A_{2A}$$ A 2 A and $$\kappa$$ κ opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

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A Survey of Graphical Page Object Detection with Deep Neural Networks

10.20944/preprints202104.0739.v1 ◽

2021 ◽

Author(s):

Jwalin Bhatt ◽

Khurram Azeem Hashmi ◽

Muhammad Zeshan Afzal ◽

Didier Stricker

Keyword(s):

Deep Learning ◽

Object Detection ◽

Conceptual Understanding ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Approaches ◽

Document Images ◽

Essential Information ◽

Current State ◽

High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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Time Series Classification with InceptionFCN

Sensors ◽

10.3390/s22010157 ◽

2021 ◽

Vol 22 (1) ◽

pp. 157

Author(s):

Saidrasul Usmankhujaev ◽

Bunyodbek Ibrokhimov ◽

Shokhrukh Baydadaev ◽

Jangwoo Kwon

Keyword(s):

Time Series ◽

Deep Learning ◽

Deep Neural Networks ◽

Learning Approaches ◽

Time Series Classification ◽

Convolutional Network ◽

Training Time ◽

Learning Techniques ◽

Significant Research ◽

Previous State

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

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A Survey of Graphical Page Object Detection with Deep Neural Networks

Applied Sciences ◽

10.3390/app11125344 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5344

Author(s):

Jwalin Bhatt ◽

Khurram Azeem Hashmi ◽

Muhammad Zeshan Afzal ◽

Didier Stricker

Keyword(s):

Deep Learning ◽

Object Detection ◽

Conceptual Understanding ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Approaches ◽

Document Images ◽

Essential Information ◽

Current State ◽

High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that make the digitization of documents viable. Since the advent of deep learning, deep learning-based object detection performance has improved many folds. This work outlines and summarizes the deep learning approaches for detecting graphical page objects in document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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Recent Progresses of Deep Learning in Drug Discovery

Current Pharmaceutical Design ◽

10.2174/1381612827666210129123231 ◽

2021 ◽

Vol 27 ◽

Author(s):

Feng Wang ◽

XiaoMin Diao ◽

Shan Chang ◽

Lei Xu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Drug Discovery ◽

Deep Neural Networks ◽

De Novo ◽

De Novo Design ◽

Learning Approaches ◽

Property Prediction ◽

Design Property ◽

Future Direction

: Deep learning, an emerging field of artificial intelligence based on neural network in machine learning, has been applied in various fields and is highly valued. Herein we mainly review several mainstream architectures in deep learning, including deep neural networks, convolutional neural networks and recurrent neural networks in the field of drug discovery. The applications of several architectures in molecular de novo design, property prediction, biomedical imaging and synthetic planning have also been explored. We also discuss the future direction of the deep learning approaches and the main challenges we need to address.

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Leveraging Deep Learning for Visual Odometry Using Optical Flow

Sensors ◽

10.3390/s21041313 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1313

Author(s):

Tejas Pandey ◽

Dexmont Pena ◽

Jonathan Byrne ◽

David Moloney

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Optical Flow ◽

Recurrent Neural Networks ◽

Deep Neural Networks ◽

Visual Odometry ◽

Camera Motion ◽

Learning Approaches ◽

Outlier Rejection ◽

Entire Trajectory

In this paper, we study deep learning approaches for monocular visual odometry (VO). Deep learning solutions have shown to be effective in VO applications, replacing the need for highly engineered steps, such as feature extraction and outlier rejection in a traditional pipeline. We propose a new architecture combining ego-motion estimation and sequence-based learning using deep neural networks. We estimate camera motion from optical flow using Convolutional Neural Networks (CNNs) and model the motion dynamics using Recurrent Neural Networks (RNNs). The network outputs the relative 6-DOF camera poses for a sequence, and implicitly learns the absolute scale without the need for camera intrinsics. The entire trajectory is then integrated without any post-calibration. We evaluate the proposed method on the KITTI dataset and compare it with traditional and other deep learning approaches in the literature.

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Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation

Acta Numerica ◽

10.1017/s0962492921000039 ◽

2021 ◽

Vol 30 ◽

pp. 203-248

Author(s):

Mikhail Belkin

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Mathematical Theory ◽

Deep Neural Networks ◽

Noisy Data ◽

Theory And Practice ◽

The Past ◽

Complex Picture ◽

Modern Machine

In the past decade the mathematical theory of machine learning has lagged far behind the triumphs of deep neural networks on practical challenges. However, the gap between theory and practice is gradually starting to close. In this paper I will attempt to assemble some pieces of the remarkable and still incomplete mathematical mosaic emerging from the efforts to understand the foundations of deep learning. The two key themes will be interpolation and its sibling over-parametrization. Interpolation corresponds to fitting data, even noisy data, exactly. Over-parametrization enables interpolation and provides flexibility to select a suitable interpolating model.As we will see, just as a physical prism separates colours mixed within a ray of light, the figurative prism of interpolation helps to disentangle generalization and optimization properties within the complex picture of modern machine learning. This article is written in the belief and hope that clearer understanding of these issues will bring us a step closer towards a general theory of deep learning and machine learning.

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GinJinn2: Object detection and segmentation for ecology and evolution

10.1101/2021.08.20.457033 ◽

2021 ◽

Author(s):

Tankred Ott ◽

Ulrich Lautenschlager

Keyword(s):

Deep Learning ◽

Object Detection ◽

Image Data ◽

Systematic Research ◽

Herbarium Specimens ◽

List Type ◽

Learning Approaches ◽

Advanced Analysis ◽

User Friendly ◽

Modern Machine

AbstractProper collection and preparation of empirical data still represent one of the most important, but also expensive steps in ecological and evolutionary/systematic research. Modern machine learning approaches, however, have the potential to automate a variety of tasks, which until recently could only be performed manually. Unfortunately, the application of such methods by researchers outside the field is hampered by technical difficulties, some of which, we believe, can be avoided.Here, we present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data. Besides providing a convenient command-line interface to existing software libraries, it comprises several additional tools for data handling, pre- and postprocessing, and building advanced analysis pipelines.We demonstrate the application of GinJinn2 for biological purposes using four exemplary analyses, namely the evaluation of seed mixtures, detection of insects on glue traps, segmentation of stomata, and extraction of leaf silhouettes from herbarium specimens.GinJinn2 will enable users with a primary background in biology to apply deep learning-based methods for object detection and segmentation in order to automate feature extraction from image data.

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Monocular Depth Estimation using Transfer learning-An Overview

E3S Web of Conferences ◽

10.1051/e3sconf/202130901069 ◽

2021 ◽

Vol 309 ◽

pp. 01069

Author(s):

K. Swaraja ◽

V. Akshitha ◽

K. Pranav ◽

B. Vyshnavi ◽

V. Sai Akhil ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Deep Neural Networks ◽

Depth Estimation ◽

Depth Information ◽

Learning Approaches ◽

Learning Network ◽

Depth Maps ◽

Ill Posed ◽

Monocular Depth

Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated.

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