scholarly journals How to train your differentiable filter

2021 ◽  
Author(s):  
Alina Kloss ◽  
Georg Martius ◽  
Jeannette Bohg
Keyword(s):  
Bayesian Filtering ◽  
Learning Approaches ◽  
Filtering Algorithms ◽  
Recursive Filtering ◽  
Process Dynamics ◽  
Noise Characteristics ◽  
Complex Models ◽  
End To End ◽  
Robotic Applications ◽  
And Training

AbstractIn many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of recursive filtering algorithms. In this work, we investigate the advantages of differentiable filters (DFs) over both unstructured learning approaches and manually-tuned filtering algorithms, and provide practical guidance to researchers interested in applying such differentiable filters. For this, we implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. Specifically, we (i) evaluate different implementation choices and training approaches, (ii) investigate how well complex models of uncertainty can be learned in DFs, (iii) evaluate the effect of end-to-end training through DFs and (iv) compare the DFs among each other and to unstructured LSTM models.

Structured Reporting in Musculoskeletal Radiology

2021 ◽  
Vol 25 (05) ◽  
pp. 641-645
Author(s):  
Ajay Kohli ◽  
Samantha Castillo ◽  
Uma Thakur ◽  
Avneesh Chhabra
Keyword(s):  
Manufacturing Industry ◽  
Service Industry ◽  
Quality Standards ◽  
Structured Reporting ◽  
Learning Approaches ◽  
Musculoskeletal Radiology ◽  
Future Directions ◽  
Future Data ◽  
Service Departments ◽  
And Training

AbstractMusculoskeletal (MSK) radiologists are predominantly consultants in the service departments of health care. Unlike the manufacturing industry, quality controls are difficult to institute in a service industry and more variability is expected. Structured reporting is a unique way to institute quality standards, and by using the checklist approach with uniform terminology, it can lead to more homogeneity and consistency of reporting, concise lexicon use within and across practices, minimization of errors, enhancement of divisional and departmental branding, improvement of interdisciplinary communications, and future data mining. We share our experience from more than a decade of structured reporting in the domain of MSK radiology, our practice standards, and how reporting has evolved in our MSK practice. Further discussions include future directions aided by machine learning approaches with augmented reality and the possibility of virtual fellowship and training using consistent lexicons and structured reporting.

Machine Learning and Human Factors: Status, Applications, and Future Directions

2018 ◽  
Vol 62 (1) ◽  
pp. 135-138 ◽  
Author(s):  
Nathan Lau ◽  
Lex Fridman ◽  
Brett J. Borghetti ◽  
John D. Lee
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Interaction Design ◽  
Intelligent Systems ◽  
Consumer Products ◽  
Learning Approaches ◽  
Future Directions ◽  
Industrial Systems ◽  
Human Factors Research ◽  
And Training

As machine learning approaches ubiquity in industrial systems and consumer products, human factors research must attend to machine learning, specifically on how intelligent systems built on machine learning are different from early generations of automated systems, and what these differences mean for human-system interaction, design, evaluation and training. This panel invites five researchers in different domains to discuss how human factors can contribute to machine learning research and applications, as well as how machine learning presents both challenges and contributions for human factors.

scholarly journals DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

2020 ◽  
Vol 34 (01) ◽  
pp. 598-605
Author(s):  
Chaoran Cheng ◽  
Fei Tan ◽  
Zhi Wei
Keyword(s):  
Deep Learning ◽  
Large Data ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Learning Approach ◽  
Learning Approaches ◽  
Genomic Variants ◽  
Low Resource ◽  
End To End ◽  
Genomic Variant

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.

Fast, yet robust end-to-end camera pose estimation for robotic applications

2020 ◽  
Author(s):  
Zahra Kamranian ◽  
Hamid Sadeghian ◽  
Ahmad Reza Naghsh Nilchi ◽  
Mehran Mehrandezh
Keyword(s):  
Pose Estimation ◽  
Camera Pose Estimation ◽  
Camera Pose ◽  
End To End ◽  
Robotic Applications

scholarly journals An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

2020 ◽  
Vol 34 (01) ◽  
pp. 303-311 ◽  
Author(s):  
Sicheng Zhao ◽  
Yunsheng Ma ◽  
Yang Gu ◽  
Jufeng Yang ◽  
Tengfei Xing ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
State Of The Art ◽  
Source Code ◽  
Cross Entropy ◽  
Attention Network ◽  
Audio Features ◽  
End To End ◽  
3D Cnn ◽  
And Training

Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extracting visual and/or audio features and training classifiers. In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classification loss, i.e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https://github.com/maysonma/VAANet.

scholarly journals Manajemen Kelas Unggulan di MTS Negeri 3 Pemalang

2020 ◽  
Vol 4 (2) ◽  
pp. 105-120
Author(s):  
Rahmatun Nida Azkiyani ◽  
Novan Ardy Wiyani ◽  
Ahmad Sahnan
Keyword(s):  
School Principal ◽  
Learning Approaches ◽  
Learning Tools ◽  
Research Subjects ◽  
Research Information ◽  
Class Management ◽  
Vice Principal ◽  
Media Learning ◽  
Infrastructure Maintenance ◽  
And Training

This study aims to describe about superior class management in MTs Negeri 3 Pemalang. This research was conducted using a qualitative approach to the type of phenomenological research. Information on research subjects was obtained through interviews, observations and documentation of the Head Master of MTs 3 Pemalang, vice principal of curriculum, vice principal in student affairs, excellent class teachers, and superior class students. While the data analysis technic used consists of data reduction, data display, and drawing conclusions. The results of this study, that the superior class of MTs Negeri 3 Pemalang has been implemented optimally is characterized by the formulation of the objectives of the superior class compiled by a team consisting of the Principal, the Board of Teachers along with a superior class tutor. The formulation of superior class regulations is carried out carefully by involving important elements in the school namely the headmaster of the madrasa, all teachers, counceling teachers, and committees. The development of superior classroom learning services is characterized by learning approaches, learning methods, learning media, learning tools and solutions to overcome obstacles in implementing learning. The development of excellent class facilities and infrastructure is marked by the planning and analysis of needs, procurement of facilities and infrastructure, maintenance of facilities and infrastructure and solutions to overcome the management of superior class infrastructure. The development of superior class teachers is characterized by recruitment, coaching and training as well as providing solutions to overcome obstacles in the implementation of competency of superior class teachers. Supervision of superior class management is marked by the supervision of learning tools by the School Principal.

scholarly journals Secure machine learning against adversarial samples at test time

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Jing Lin ◽  
Laurent L. Njilla ◽  
Kaiqi Xiong
Keyword(s):  
Machine Learning ◽  
Parallel Implementation ◽  
Standard Test ◽  
Test Time ◽  
Test Accuracy ◽  
Learning Approaches ◽  
Complex Models ◽  
Projected Gradient Descent ◽  
Adversarial Example ◽  
Human Eyes

AbstractDeep neural networks (DNNs) are widely used to handle many difficult tasks, such as image classification and malware detection, and achieve outstanding performance. However, recent studies on adversarial examples, which have maliciously undetectable perturbations added to their original samples that are indistinguishable by human eyes but mislead the machine learning approaches, show that machine learning models are vulnerable to security attacks. Though various adversarial retraining techniques have been developed in the past few years, none of them is scalable. In this paper, we propose a new iterative adversarial retraining approach to robustify the model and to reduce the effectiveness of adversarial inputs on DNN models. The proposed method retrains the model with both Gaussian noise augmentation and adversarial generation techniques for better generalization. Furthermore, the ensemble model is utilized during the testing phase in order to increase the robust test accuracy. The results from our extensive experiments demonstrate that the proposed approach increases the robustness of the DNN model against various adversarial attacks, specifically, fast gradient sign attack, Carlini and Wagner (C&W) attack, Projected Gradient Descent (PGD) attack, and DeepFool attack. To be precise, the robust classifier obtained by our proposed approach can maintain a performance accuracy of 99% on average on the standard test set. Moreover, we empirically evaluate the runtime of two of the most effective adversarial attacks, i.e., C&W attack and BIM attack, to find that the C&W attack can utilize GPU for faster adversarial example generation than the BIM attack can. For this reason, we further develop a parallel implementation of the proposed approach. This parallel implementation makes the proposed approach scalable for large datasets and complex models.

scholarly journals Text Classification Algorithms: A Survey

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 150 ◽  
Author(s):  
Kowsari ◽  
Jafari Meimandi ◽  
Heidarysafa ◽  
Mendu ◽  
Barnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Text Classification ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Linear Relationships ◽  
Reduction Methods ◽  
Complex Models

In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts in many applications. Many machine learning approaches have achieved surpassingresults in natural language processing. The success of these learning algorithms relies on their capacityto understand complex models and non-linear relationships within data. However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. In thispaper, a brief overview of text classification algorithms is discussed. This overview covers differenttext feature extractions, dimensionality reduction methods, existing algorithms and techniques, andevaluations methods. Finally, the limitations of each technique and their application in real-worldproblems are discussed.

scholarly journals Students’ acceptance of e-learning approaches in Laboratory Animal Science Training

2019 ◽  
Vol 54 (5) ◽  
pp. 487-497
Author(s):  
Andreia Costa ◽  
Ana Costa ◽  
I. Anna S. Olsson
Keyword(s):  
Laboratory Animal ◽  
Flipped Classroom ◽  
Quality Criteria ◽  
Practical Training ◽  
Learning Approaches ◽  
Animal Science ◽  
Starting Point ◽  
E Learning ◽  
And Training ◽  
Laboratory Animal Science

Different online courses and training programs in Laboratory Animal Science (LAS) have emerged across Europe in recent years. E-learning appears to be a promising solution to achieve flexibility in training while meeting the quality criteria of demanding programs in short training periods. However, little is known about how students perceive e-learning in this context, and there is also a lack of specific and valid instruments to measure this perception. Within an exploratory study framework, the e-learning perception of 229 participants in 15 courses in Portugal using two different online training formats, flipped classroom and full online theoretical training, was assessed. For this purpose, the Questionnaire of E-learning Acceptance (QELA), a 32-item accordance Likert-type scale comprising five subscales was developed to explore the following: how participant perceive e-learning, satisfaction with organization and contents, perception of e-learning relevance for the time management, and its influence for practical training. In general, e-learning was well accepted and perceived to work well and be useful by the majority of courses participants, independently of the course level and e-learning format approach. These results indeed suggest that integration of e-learning is useful in LAS training. We also propose the QELA as a starting point for development and implementation of specific instruments to assess e-learning acceptance in LAS across a wider range of geographical and training contexts.

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