scholarly journals Recurrent Neural Networks for Hierarchically Mapping Human-Robot Poses

2021 ◽  
Vol 3 (1) ◽  
pp. 99-120
Author(s):  
Zainab Al-Qurashi ◽  
Brian D. Ziebart
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Temporal Structure ◽  
Hybrid Approach ◽  
Learning System ◽  
Training Dataset ◽  
Human Hand ◽  
Hand Position ◽  
Position And Orientation

To perform many critical manipulation tasks successfully, human-robot mimicking systems should not only accurately copy the position of a human hand, but its orientation as well. Deep learning methods trained from pairs of corresponding human and robot poses offer one promising approach for constructing a human-robot mapping to accomplish this. However, ignoring the spatial and temporal structure of this mapping makes learning it less effective. We propose two different hierarchical architectures that leverage the structural and temporal human-robot mapping. We partially separate the robotic manipulator's end-effector position and orientation while considering the mutual coupling effects between them. This divides the main problem---making the robot match the human's hand position and mimic its orientation accurately along an unknown trajectory---into several sub-problems. We address these using different recurrent neural networks (RNNs) with Long-Short Term Memory (LSTM) that we combine and train hierarchically based on the coupling over the aspects of the robot that each controls. We evaluate our proposed architectures using a virtual reality system to track human table tennis motions and compare with single artificial neural network (ANN) and RNN models. We compare the benefits of using deep learning neural networks with and without our architectures and find smaller errors in position and orientation, along with increased flexibility in wrist movement are obtained by our proposed architectures. Also, we propose a hybrid approach to collect the training dataset. The hybrid training dataset is collected by two approaches when the robot mimics human motions (standard learn from demonstrator LfD) and when the human mimics robot motions (LfDr). We evaluate the hybrid training dataset and show that the performance of the machine learning system trained by the hybrid training dataset is better with less error and faster training time compared to using the collected dataset using standard LfD approach.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Teaching recurrent neural networks to infer global temporal structure from local examples

2021 ◽  
Vol 3 (4) ◽  
pp. 316-323 ◽  
Author(s):  
Jason Z. Kim ◽  
Zhixin Lu ◽  
Erfan Nozari ◽  
George J. Pappas ◽  
Danielle S. Bassett
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Temporal Structure

scholarly journals Classification & Detection of Vehicles using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 181-189
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Transportation Systems ◽  
Coarse Grained ◽  
Vehicle Classification ◽  
Training Dataset ◽  
License Plate ◽  
Original Dataset ◽  
Detection Of Objects

The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. However, theexisting methods of vehicle classification and detection are highly complex which provides coarse-grained outcomesbecause of underfitting or overfitting of the model. Due toadvanced accomplishmentsof the Deep Learning, it was efficiently implemented to image classification and detection of objects. This proposed paper come up with a new approach which makes use of convolutional neural networks concept in Deep Learning.It consists of two steps: i) vehicle classification ii) vehicle license plate recognition. Numerous classicmodules of neural networks hadbeen implemented in training and testing the vehicle classification and detection of license plate model, such as CNN (convolutional neural networks), TensorFlow, and Tesseract-OCR. The suggestedtechnique candetermine the vehicle type, number plate and other alternative dataeffectively. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original dataset (training) and enriched dataset (testing), this customized model(algorithm) can achieve best outcomewith a standard accuracy of around 97.32% inclassification and detection of vehicles. By enlarging the quantity of the training dataset, the loss function and mislearning rate declines progressively. Therefore, this proposedmodelwhich uses DeepLearning hadbetterperformance and flexibility. When compared to outstandingtechniques in the strategicImage datasets, this deep learning modelscan gethigher competitor outcomes. Eventually, the proposed system suggests modern methods for advancementof the customized model and forecasts the progressivegrowth of deep learningperformance in the explorationof artificial intelligence (AI) &machine learning (ML) techniques.

scholarly journals Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

2021 ◽  
Vol 17 (9) ◽  
pp. e1009345
Author(s):  
Zhengqiao Zhao ◽  
Stephen Woloszynek ◽  
Felix Agbavor ◽  
Joshua Chang Mell ◽  
Bahrad A. Sokhansanj ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Microbial Community ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Sequence Data ◽  
Community Sample ◽  
Marker Genes ◽  
Link Type

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

scholarly journals Image Compression Based on Deep Learning: A Review

2021 ◽  
pp. 62-76
Author(s):  
Hajar Maseeh Yasin ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Compression ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Review Paper ◽  
High Accuracy ◽  
Machine Learning Methods ◽  
Digital Era ◽  
Different Types

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.

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