scholarly journals On the Robustness of Active Learning

10.29007/thws ◽  
2019 ◽  
Author(s):  
Lukas Hahn ◽  
Lutz Roese-Koerner ◽  
Peet Cremer ◽  
Urs Zimmermann ◽  
Ori Maoz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Active Learning ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Diversity Index ◽  
Confusion Matrix ◽  
Sample Selection ◽  
Machine Learning Algorithm ◽  
Data Requirements

Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with. When applied correctly, it can be a very powerful tool to counteract the immense data requirements of Artificial Neural Networks. However, we find that it is often applied with not enough care and domain knowledge. As a consequence, unrealistic hopes are raised and transfer of the experimental results from one dataset to another becomes unnecessarily hard.In this work we analyse the robustness of different Active Learning methods with respect to classifier capacity, exchangeability and type, as well as hyperparameters and falsely labelled data. Experiments reveal possible biases towards the architecture used for sample selection, resulting in suboptimal performance for other classifiers. We further propose the new ”Sum of Squared Logits” method based on the Simpson diversity index and investigate the effect of using the confusion matrix for balancing in sample selection.

scholarly journals Interpretation of Neural Networks Is Fragile

2019 ◽  
Vol 33 ◽  
pp. 3681-3688 ◽  
Author(s):  
Amirata Ghorbani ◽  
Abubakar Abid ◽  
James Zou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Input Data ◽  
Learning Algorithm ◽  
Hessian Matrix ◽  
Machine Learning Algorithm ◽  
Feature Importance ◽  
Adversarial Attack ◽  
Measurement Biases

In order for machine learning to be trusted in many applications, it is critical to be able to reliably explain why the machine learning algorithm makes certain predictions. For this reason, a variety of methods have been developed recently to interpret neural network predictions by providing, for example, feature importance maps. For both scientific robustness and security reasons, it is important to know to what extent can the interpretations be altered by small systematic perturbations to the input data, which might be generated by adversaries or by measurement biases. In this paper, we demonstrate how to generate adversarial perturbations that produce perceptively indistinguishable inputs that are assigned the same predicted label, yet have very different interpretations. We systematically characterize the robustness of interpretations generated by several widely-used feature importance interpretation methods (feature importance maps, integrated gradients, and DeepLIFT) on ImageNet and CIFAR-10. In all cases, our experiments show that systematic perturbations can lead to dramatically different interpretations without changing the label. We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly susceptible to adversarial attack. Our analysis of the geometry of the Hessian matrix gives insight on why robustness is a general challenge to current interpretation approaches.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals An Effective Integration of Domain Knowledge in-to Deep Neural Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 507-509
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Ease Of Use ◽  
The Past ◽  
Complex Models ◽  
Effective Integration

Machine learning in recent years has become an integral part of our day to day life and the ease of use has improved a lot in the past decade.There are various ways to make the model to work in smaller devices.A modest method to advance any machine learning algorithm to work in smaller devices is to provide the output of large complex models as input to smaller models which can be easily deployed into mobile phones .We provided a framework where the large models can even learn the domain knowledge which is integrated as first-order logic rules and explicitly includes that knowledge into the smaller model by simultaneously training of both the models.This can be achieved by transfer learning where the knowledge learned by one model can be used to teach the other model.Domain knowledge integration is the most critical part here and it can be done by using some of the constraint principles where the scope of the data is reduced based upon the constraints mentioned. One of the best representation of domain knowledge is logic rules where the knowledge is encoded as predicates.This framework provides a way to integrate human knowledge into deep neural networks that can be easily deployed into any devices.

scholarly journals Deep Machine Learning and Neural Networks: An Overview

2017 ◽  
Vol 6 (2) ◽  
pp. 66 ◽  
Author(s):  
Chandrahas Mishra ◽  
D. L. Gupta
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Application Area ◽  
Intelligent Machines ◽  
Different Types ◽  
Dynamic Examination

Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.

scholarly journals Prediction of Network Attacks using Bio-Inspired Machine Learning Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 5482-5485
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Computer Science ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Algorithm Engineering ◽  
Machine Learning Algorithm ◽  
Network Intrusion

Most of the times, data is created for the Intrusion Detection System (IDS) only when the set of all real working environments are explored under all the possibilities of attacks, which is an expensive task. Network Intrusion Detection software shields a system and computer network from staff and non-authorized users. The detector’s ultimate task is to build a foreboding classifier (i.e. a model) which would help in distinguishing between friendly and non-friendly connections, known as attacks or intrusions.This problem in network sectors is prevented by predicting whether the connection is attacked or not attacked from the dataset. We are using i.e. KDDCup99 using bio inspired machine learning techniques (like Artificial Neural Network). Bio inspired algorithm is a game changer in computer science. The extent of this field is really magnificent as compared to nature around it, complications of computer science are only a subset of it, opening a new era in next generation computing, modelling and algorithm engineering. The aim is to investigate bio inspired machine learning based techniques for better packet connection transfers forecasting by prediction results in best accuracy and to propose this machine learning-based method to accurately predict the DOS, R2L, U2R, Probe and overall attacks by predicting results in the form of best accuracy from comparing supervised classification machine learning algorithms. Furthermore, to compare and discuss the performance of various ML algorithms from the provided dataset with classification and evaluation report, finding and analysing the confusion matrix and for classifying data from the priority and result shows that the effectiveness of the proposed system i.e. bio inspired machine learning algorithm technique can be put on test with best accuracy along with precision, specificity, sensitivity, F1 Score and Recall

Genetic Programming as Supervised Machine Learning Algorithm

2018 ◽  
pp. 48-101
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
High Performance ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Computer Programs ◽  
Multiclass Classification ◽  
Supervised Machine Learning ◽  
Learning Problems ◽  
Machine Learning Algorithm

This chapter presents the theory and procedures behind supervised machine learning and how genetic programming can be applied to be an effective machine learning algorithm. Due to simple and powerful concept of computer programs, genetic programming can solve many supervised machine learning problems, especially regression and classifications. The chapter starts with theory of supervised machine learning by describing the three main groups of modelling: regression, binary, and multiclass classification. Through those kinds of modelling, the most important performance parameters and skill scores are introduced. The chapter also describes procedures of the model evaluation and construction of confusion matrix for binary and multiclass classification. The second part describes in detail how to use genetic programming in order to build high performance GP models for regression and classifications. It also describes the procedure of generating computer programs for binary and multiclass calcification problems by introducing the concept of predefined root node.

scholarly journals Photonic reservoir computing using delay dynamical systems

Photoniques ◽  
2020 ◽  
pp. 45-48
Author(s):  
Piotr Antonik ◽  
Serge Massar ◽  
Guy Van Der Sande
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Dynamical Systems ◽  
Recent Progress ◽  
Learning Algorithm ◽  
Reservoir Computing ◽  
Machine Learning Algorithm ◽  
Hardware Implementations ◽  
All Optical

The recent progress in artificial intelligence has spurred renewed interest in hardware implementations of neural networks. Reservoir computing is a powerful, highly versatile machine learning algorithm well suited for experimental implementations. The simplest highperformance architecture is based on delay dynamical systems. We illustrate its power through a series of photonic examples, including the first all optical reservoir computer and reservoir computers based on lasers with delayed feedback. We also show how reservoirs can be used to emulate dynamical systems. We discuss the perspectives of photonic reservoir computing.

scholarly journals A Solution to Cartpole using Neural Networks and Tensorflow

2019 ◽  
Vol 8 (4) ◽  
pp. 920-924
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Computational Efficiency ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Techniques ◽  
The Right ◽  
Algorithm Benchmarking

Machine learning is not quite a new topic for discussion these days. A lot of enthusiasts excel in this field. The problem just lies with the beginners who lack just the right amount of intuition in to step ahead in this field. This paper is all about finding a simple enough solution to this issue through an example problem Cart-Pole an Open AI Gym’s classic Machine Learning algorithm benchmarking tool. The contents here will provide a perception to Machine Learning and will help beginners get familiar with the field quite a lot. Machine Learning techniques like Regression which further includes Linear and Logistic Regression, forming the basics of Neural Networks using familiar terms from Logistic regression would be mentioned here. Along with using TensorFlow, a Google’s project initiative which is widely used today for computational efficiency would be all of the techniques used here to solve the trivial game Cart-Pole

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