scholarly journals Learn from Concepts: Towards the Purified Memory for Few-shot Learning

2021 ◽  
Author(s):  
Xuncheng Liu ◽  
Xudong Tian ◽  
Shaohui Lin ◽  
Yanyun Qu ◽  
Lizhuang Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Fundamental Problem ◽  
Human Beings ◽  
Learning Methods ◽  
Graph Augmentation ◽  
Vast Number ◽  
Memory Mechanism ◽  
Recognition Process ◽  
The Past

Human beings have a great generalization ability to recognize a novel category by only seeing a few number of samples. This is because humans possess the ability to learn from the concepts that already exist in our minds. However, many existing few-shot approaches fail in addressing such a fundamental problem, {\it i.e.,} how to utilize the knowledge learned in the past to improve the prediction for the new task. In this paper, we present a novel purified memory mechanism that simulates the recognition process of human beings. This new memory updating scheme enables the model to purify the information from semantic labels and progressively learn consistent, stable, and expressive concepts when episodes are trained one by one. On its basis, a Graph Augmentation Module (GAM) is introduced to aggregate these concepts and knowledge learned from new tasks via a graph neural network, making the prediction more accurate. Generally, our approach is model-agnostic and computing efficient with negligible memory cost. Extensive experiments performed on several benchmarks demonstrate the proposed method can consistently outperform a vast number of state-of-the-art few-shot learning methods.

Common Sense Propositions

Philosophy ◽  
1973 ◽  
Vol 48 (186) ◽  
pp. 363-379
Author(s):  
A. C. Ewing
Keyword(s):  
Common Sense ◽  
Religious Beliefs ◽  
Physical Object ◽  
Human Beings ◽  
Vast Number ◽  
Know How ◽  
Justified Belief ◽  
The Past ◽  
The Right ◽  
Past Experiences

Philosophers have not been sceptical only about metaphysics or religious beliefs. There are a great number of other beliefs generally held which they have had at least as much difficulty in justifying, and in the present article I ask questions as to the right philosophical attitude to these beliefs in cases where to our everyday thought they seem so obvious as to be a matter of the most ordinary common sense. A vast number of propositions go beyond what is merely empirical and cannot be seen to be logically necessary but are still believed by everybody in their daily life. Into this class fall propositions about physical things, other human minds and even propositions about one's own past experiences based on memory, for we are not now ‘observing’ our past. The phenomenalist does not escape the difficulty about physical things, for he reduces physical object propositions, in so far as true, not merely to propositions about his own actual experience but to propositions about the experiences of other human beings in general under certain conditions, and he cannot either observe or logically prove what the experiences of other people are or what even his own would be under conditions which have not yet been fulfilled. What is the philosopher to say about such propositions? Even Moore, who insisted so strongly that we knew them, admitted that we did not know how we knew them. The claim which a religious man makes to a justified belief that is neither a matter of purely empirical perception nor formally provable is indeed by no means peculiar to the religious. It is made de facto by everybody in his senses, whether or not he realizes that he is doing so. There is indeed a difference: while everyone believes in the existence of other human beings and in the possibility of making some probable predictions about the future from the past, not everybody holds religious beliefs, and although this does not necessarily invalidate the claim it obviously weakens it.

scholarly journals BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Limin Jiang ◽  
Jingjun Zhang ◽  
Ping Xuan ◽  
Quan Zou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Biological Function ◽  
State Of The Art ◽  
Biological Evolution ◽  
Noncoding Rnas ◽  
The Past ◽  
Mirna Identification

MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.

scholarly journals Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

2021 ◽  
Author(s):  
Toly Chen ◽  
Yu-Cheng Lin
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Feedforward Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Forecasting Accuracy ◽  
Hit Rate ◽  
The Past ◽  
Acceptable Range ◽  
Accuracy And Precision

AbstractMost existing methods for forecasting the productivity of a factory cannot estimate the range of productivity reliably, especially when future conditions are distinct from those in the past. To address this issue, a fuzzified feedforward neural network (FFNN) approach is proposed in this study. The FFNN approach improves the forecasting precision after generating accurate fuzzy productivity forecasts. In addition, the acceptable range of a fuzzy productivity forecast is specified, based on which the sum of the memberships of actual values is maximized. In this way, the range of productivity can be precisely estimated. After applying the FFNN approach to a real case, the experimental results revealed the superiority of the FFNN approach by improving the forecasting precision, in terms of the hit rate, by 25%. Such an improvement also contributed to a better forecasting accuracy. The superiority of the FFNN approach is in the context that the accuracy of forecasting productivity is optimized only after the range of productivity has been precisely estimated. In contrast, most state-of-the-art methods focus on optimizing the forecasting accuracy, but may be ineffective without information about the range of productivity when future conditions are distinct from the past.

scholarly journals Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

2021 ◽  
Author(s):  
Akhilesh Verma ◽  
Anshadha Gupta ◽  
Mohammad Akbar ◽  
Arun Kumar Yadav ◽  
Divakar Yadav
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
State Of The Art ◽  
User Authentication ◽  
Attack Detection ◽  
Classification Error ◽  
The Public ◽  
The Past ◽  
Biometric Systems ◽  
Presentation Attack Detection

Abstract The fingerprint presentation attack is still a major challenge in biometric systems due to its increased applications worldwide. In the past, researchers used Fingerprint Presentation Attack Detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality (RIQ)-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.

scholarly journals Transductive Relation-Propagation Network for Few-shot Learning

2020 ◽  
Author(s):  
Yuqing Ma ◽  
Shihao Bai ◽  
Shan An ◽  
Wei Liu ◽  
Aishan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Learning Strategy ◽  
State Of The Art ◽  
Challenging Problem ◽  
Learning Problem ◽  
Graph Node ◽  
Learning Methods ◽  
Transductive Learning ◽  
Benchmark Datasets ◽  
Propagation Network

Few-shot learning, aiming to learn novel concepts from few labeled examples, is an interesting and very challenging problem with many practical advantages. To accomplish this task, one should concentrate on revealing the accurate relations of the support-query pairs. We propose a transductive relation-propagation graph neural network (TRPN) to explicitly model and propagate such relations across support-query pairs. Our TRPN treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intra-class commonality and inter-class uniqueness, to guide the relation propagation in the graph, generating the discriminative relation embeddings for support-query pairs. A pseudo relational node is further introduced to propagate the query characteristics, and a fast, yet effective transductive learning strategy is devised to fully exploit the relation information among different queries. To the best of our knowledge, this is the first work that explicitly takes the relations of support-query pairs into consideration in few-shot learning, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.

scholarly journals Tri-net for Semi-Supervised Deep Learning

2018 ◽  
Author(s):  
Dong-Dong Chen ◽  
Wei Wang ◽  
Wei Gao ◽  
Zhi-Hua Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Error Rate ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Fine Tuning ◽  
Learning Methods ◽  
Model Initialization

Deep neural networks have witnessed great successes in various real applications, but it requires a large number of labeled data for training. In this paper, we propose tri-net, a deep neural network which is able to use massive unlabeled data to help learning with limited labeled data. We consider model initialization, diversity augmentation and pseudo-label editing simultaneously. In our work, we utilize output smearing to initialize modules, use fine-tuning on labeled data to augment diversity and eliminate unstable pseudo-labels to alleviate the influence of suspicious pseudo-labeled data. Experiments show that our method achieves the best performance in comparison with state-of-the-art semi-supervised deep learning methods. In particular, it achieves 8.30% error rate on CIFAR-10 by using only 4000 labeled examples.

scholarly journals Identifying Malaria Infection in Red Blood Cells using Optimized Step-Increase Convolutional Neural Network Model

2019 ◽  
Vol 8 (9S) ◽  
pp. 813-818 ◽  
Keyword(s):  
Neural Network ◽  
Malaria Infection ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Vast Number ◽  
Learning Techniques ◽  
Proposed Model ◽  
Blood Smears ◽  
Network Approaches

A vast number of image processing and neural network approaches are currently being utilized in the analysis of various medical conditions. Malaria is a disease which can be diagnosed by examining blood smears. But when it is examined manually by the microscopist, the accuracy of diagnosis can be error-prone because it depends upon the quality of the smear and the expertise of microscopist in examining the smears. Among the various machine learning techniques, convolutional neural networks (CNN) promise relatively higher accuracy. We propose an Optimized Step-Increase CNN (OSICNN) model to classify red blood cell images taken from thin blood smear samples into infected and non-infected with the malaria parasite. The proposed OSICNN model consists of four convolutional layers and is showing comparable results when compared with other state of the art models. The accuracy of identifying parasite in RBC has been found to be 98.3% with the proposed model.

scholarly journals Technology and human finitude

2015 ◽  
Vol 27 (40) ◽  
pp. 245
Author(s):  
Andrew Feenberg
Keyword(s):  
Fundamental Problem ◽  
Human Action ◽  
Past Century ◽  
Human Beings ◽  
The Past ◽  
Theoretic Concept ◽  
The Many ◽  
Greek Source ◽  
Human Finitude

In this text I discuss the fundamental problem of human finitude. This is an issue that comes up in both sources of Western ethical tradition, both the Judaic and the Greek source. The ancient wisdom teaches human finitude and enjoins human beings to avoid hubris, the belief that they are gods. Despite, or rather because of the many advances in technology that have occurred in the past century, we can still draw on this tradition for wisdom. The text is divided into three parts: ontological finitude, epistemological finitude and democracy as recognition of finitude. A systems-theoretic concept of human action and the concept of “entangled hierarchy” are introduced to explain the relevance of finitude to technology.

Cyrus’s Imperial Household: an Aristotelian Reading of Xenophon’s Cyropaedia

2008 ◽  
Vol 25 (1) ◽  
pp. 31-62 ◽  
Author(s):  
Christopher Whidden
Keyword(s):  
Fundamental Problem ◽  
Human Beings ◽  
Persian Empire ◽  
The Past ◽  
Peace And Security ◽  
Thoughtful Critique

Xenophon’s Cyropaedia is a fictional account of the life of Cyrus the Great, the founder of the Persian Empire. This article argues that reading the Cyropaedia through an Aristotelian lens provides a useful means by which to understand Xenophon’s analysis of Cyrus’s empire. On an Aristotelian reading, a crucial facet of Cyrus’s knowledge is his view that the household provides an appropriate model by which to found and govern an empire. By incorporating many nations into what I call his ‘imperial household’, Cyrus finds a way to avoid what Xenophon sees as the fundamental problem of political rule, which is that human beings do not wish to be ruled by others and eventually revolt against their rulers. But in contrast to all previous rulers known to Xenophon, Cyrus secures his subjects’ obedience. He does so by treating them as women, children, and slaves, each of whom looks to him as the head of the household. Under Cyrus, the perpetual political revolutions Xenophon describes thus become a thing of the past, at least so long as Cyrus is alive to preside over his imperial household. But Xenophon also suggests that order, peace, and security in the empire come at a cost. In order to keep his subjects in line, Cyrus as leader must distort and do violence to their humanity. Read carefully, the Cyropaedia thus provides a thoughtful critique of imperial ambition and empire.

scholarly journals Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 409
Author(s):  
Panagiotis Kasnesis ◽  
Lazaros Toumanidis ◽  
Charalampos Z. Patrikakis
Keyword(s):  
Social Networks ◽  
Comparative Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Language Models ◽  
Modular Approach ◽  
Human Beings ◽  
Fake News ◽  
The Past ◽  
Subjectivity Analysis

The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post’s content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human beings. To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post’s subjectivity and the stance provided by the replies it has received to date, letting the user decide whether (s)he trusts or does not trust the provided information. To this end, we fine-tuned state-of-the-art transformer-based language models and compared their performance with previous related work on stance detection and subjectivity analysis. Finally, we discuss the obtained results.

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