Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects

2019 ◽  
Vol 3 (4) ◽  
pp. 1-80 ◽  
Author(s):  
Sampo Kuutti ◽  
Saber Fallah ◽  
Richard Bowden ◽  
Phil Barber
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Control Algorithms ◽  
Future Prospects

Hardware-in-the-Loop Platform for Testing Autonomous Vehicle Control Algorithms

2019 ◽  
Author(s):  
Jani Erik Heikkinen ◽  
Salimzhan Gafurov ◽  
Sergey Kopylov ◽  
Tatiana Minav ◽  
Sergey Grebennikov ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Control Algorithms ◽  
Hardware In The Loop

scholarly journals A Survey of Deep Learning Applications to Autonomous Vehicle Control

2020 ◽  
pp. 1-22 ◽  
Author(s):  
Sampo Kuutti ◽  
Richard Bowden ◽  
Yaochu Jin ◽  
Phil Barber ◽  
Saber Fallah
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Vehicle Control

scholarly journals SP-ILC: Concurrent Single-Pixel Imaging, Object Location, and Classification by Deep Learning

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 400
Author(s):  
Zhe Yang ◽  
Yu-Ming Bai ◽  
Li-Da Sun ◽  
Ke-Xin Huang ◽  
Jun Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Scheme ◽  
Autonomous Vehicle ◽  
Sampling Rate ◽  
Vehicle Control ◽  
Object Location ◽  
Concurrent Processes ◽  
Effective Use ◽  
High Degree ◽  
Single Pixel

We propose a concurrent single-pixel imaging, object location, and classification scheme based on deep learning (SP-ILC). We used multitask learning, developed a new loss function, and created a dataset suitable for this project. The dataset consists of scenes that contain different numbers of possibly overlapping objects of various sizes. The results we obtained show that SP-ILC runs concurrent processes to locate objects in a scene with a high degree of precision in order to produce high quality single-pixel images of the objects, and to accurately classify objects, all with a low sampling rate. SP-ILC has potential for effective use in remote sensing, medical diagnosis and treatment, security, and autonomous vehicle control.

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>

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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