189,052 research outputs found
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks
Explainable Artificial Intelligence (XAI) aims to make learning machines less
opaque, and offers researchers and practitioners various tools to reveal the
decision-making strategies of neural networks. In this work, we investigate how
XAI methods can be used for exploring and visualizing the diversity of feature
representations learned by Bayesian Neural Networks (BNNs). Our goal is to
provide a global understanding of BNNs by making their decision-making
strategies a) visible and tangible through feature visualizations and b)
quantitatively measurable with a distance measure learned by contrastive
learning. Our work provides new insights into the \emph{posterior} distribution
in terms of human-understandable feature information with regard to the
underlying decision making strategies. The main findings of our work are the
following: 1) global XAI methods can be applied to explain the diversity of
decision-making strategies of BNN instances, 2) Monte Carlo dropout with
commonly used Dropout rates exhibit increased diversity in feature
representations compared to the multimodal posterior approximation of
MultiSWAG, 3) the diversity of learned feature representations highly
correlates with the uncertainty estimate for the output and 4) the inter-mode
diversity of the multimodal posterior decreases as the network width increases,
while the intra mode diversity increases. These findings are consistent with
the recent Deep Neural Networks theory, providing additional intuitions about
what the theory implies in terms of humanly understandable concepts.Comment: 16 pages, 18 figure
Machine Learning Techniques for Lung Cancer Risk Prediction using Text Dataset
The early symptoms of lung cancer, a serious threat to human health, are comparable to those of the common cold and bronchitis. Clinical professionals can use machine learning techniques to customize screening and prevention strategies to the unique needs of each patient, potentially saving lives and enhancing patient care. Researchers must identify linked clinical and demographic variables from patient records and further pre-process and prepare the dataset for training a machine-learning model in order to properly predict the development of lung cancer. The goal of the study is to develop a precise and understandable machine learning (ML) model for early lung cancer prediction utilizing demographic and clinical variables, as well as to contribute to the growing field of medical research ML application that may improve healthcare outcomes. In order to create the most effective and precise predictive model, machine learning techniques like Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbor (KNN), and Naive Bayes were utilized in this article
Towards Structured Analysis of Broadcast Badminton Videos
Sports video data is recorded for nearly every major tournament but remains
archived and inaccessible to large scale data mining and analytics. It can only
be viewed sequentially or manually tagged with higher-level labels which is
time consuming and prone to errors. In this work, we propose an end-to-end
framework for automatic attributes tagging and analysis of sport videos. We use
commonly available broadcast videos of matches and, unlike previous approaches,
does not rely on special camera setups or additional sensors.
Our focus is on Badminton as the sport of interest. We propose a method to
analyze a large corpus of badminton broadcast videos by segmenting the points
played, tracking and recognizing the players in each point and annotating their
respective badminton strokes. We evaluate the performance on 10 Olympic matches
with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player
detection score ([email protected]), 97.98% player identification accuracy, and stroke
segmentation edit scores of 80.48%. We further show that the automatically
annotated videos alone could enable the gameplay analysis and inference by
computing understandable metrics such as player's reaction time, speed, and
footwork around the court, etc.Comment: 9 page
Transparency in Complex Computational Systems
Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s..
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Social Media Advertisement Outreach: Learning the Role of Aesthetics
Corporations spend millions of dollars on developing creative image-based
promotional content to advertise to their user-base on platforms like Twitter.
Our paper is an initial study, where we propose a novel method to evaluate and
improve outreach of promotional images from corporations on Twitter, based
purely on their describable aesthetic attributes. Existing works in aesthetic
based image analysis exclusively focus on the attributes of digital
photographs, and are not applicable to advertisements due to the influences of
inherent content and context based biases on outreach.
Our paper identifies broad categories of biases affecting such images,
describes a method for normalization to eliminate effects of those biases and
score images based on their outreach, and examines the effects of certain
handcrafted describable aesthetic features on image outreach. Optimizing on the
describable aesthetic features resulting from this research is a simple method
for corporations to complement their existing marketing strategy to gain
significant improvement in user engagement on social media for promotional
images.Comment: Accepted to SIGIR 201
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
Developmental psychology and neuroimaging
research identified a close link between numbers and fingers,
which can boost the initial number knowledge in children. Recent
evidence shows that a simulation of the children's embodied
strategies can improve the machine intelligence too. This article
explores the application of embodied strategies to convolutional
neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually
acquired while operating rather than being abundant and fully
available as the classical machine learning scenarios. The
experimental analyses show that the proprioceptive information
from the robot fingers can improve network accuracy in the
recognition of handwritten Arabic digits when training examples
and epochs are few. This result is comparable to brain imaging
and longitudinal studies with young children. In conclusion, these
findings also support the relevance of the embodiment in the case
of artificial agents’ training and show a possible way for the
humanization of the learning process, where the robotic body can
express the internal processes of artificial intelligence making it
more understandable for humans
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
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