245 research outputs found
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an
approach to visualize and understand the decisions made by deep neural networks
(DNNs) given a specific input. CLEAR facilitates the visualization of attentive
regions and levels of interest of DNNs during the decision-making process. It
also enables the visualization of the most dominant classes associated with
these attentive regions of interest. As such, CLEAR can mitigate some of the
shortcomings of heatmap-based methods associated with decision ambiguity, and
allows for better insights into the decision-making process of DNNs.
Quantitative and qualitative experiments across three different datasets
demonstrate the efficacy of CLEAR for gaining a better understanding of the
inner workings of DNNs during the decision-making process.Comment: Accepted at Computer Vision and Patter Recognition Workshop (CVPR-W)
on Explainable Computer Vision, 201
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Deep learning has been shown to outperform traditional machine learning
algorithms across a wide range of problem domains. However, current deep
learning algorithms have been criticized as uninterpretable "black-boxes" which
cannot explain their decision making processes. This is a major shortcoming
that prevents the widespread application of deep learning to domains with
regulatory processes such as finance. As such, industries such as finance have
to rely on traditional models like decision trees that are much more
interpretable but less effective than deep learning for complex problems. In
this paper, we propose CLEAR-Trade, a novel financial AI visualization
framework for deep learning-driven stock market prediction that mitigates the
interpretability issue of deep learning methods. In particular, CLEAR-Trade
provides a effective way to visualize and explain decisions made by deep stock
market prediction models. We show the efficacy of CLEAR-Trade in enhancing the
interpretability of stock market prediction by conducting experiments based on
S&P 500 stock index prediction. The results demonstrate that CLEAR-Trade can
provide significant insight into the decision-making process of deep
learning-driven financial models, particularly for regulatory processes, thus
improving their potential uptake in the financial industry
Understanding Anatomy Classification Through Attentive Response Maps
One of the main challenges for broad adoption of deep learning based models
such as convolutional neural networks (CNN), is the lack of understanding of
their decisions. In many applications, a simpler, less capable model that can
be easily understood is favorable to a black-box model that has superior
performance. In this paper, we present an approach for designing CNNs based on
visualization of the internal activations of the model. We visualize the
model's response through attentive response maps obtained using a fractional
stride convolution technique and compare the results with known imaging
landmarks from the medical literature. We show that sufficiently deep and
capable models can be successfully trained to use the same medical landmarks a
human expert would use. Our approach allows for communicating the model
decision process well, but also offers insight towards detecting biases.Comment: Accepted at ISBI, 201
Deformation due to Mechanical & Electromagnetic Forces in a Magneto-Micropolar Plate irradiated by Thermal Pulsed Laser
The purpose of this paper is to study the elastodynamical interactions in magneto-micropolar thermoelastic half-space considering the effect of hall current, laser heat source and rotation subjected to input ultra-laser heat source. The micropolar theory of thermoelasticity by Eringen (1966) has been used to investigate the problem. Normal mode analysis technique has been used to solve the resulting non–dimensional coupled field equations to obtain displacement, stress components and temperature distribution. Numerical computed results of all the considered variables have been shown graphically to depict the combined effect of hall current, laser heat source and rotation on the phenomena. Some particular cases of interest are also deduced from the present study
Interaction of Laser Beam with Micropolar Thermoelastic Solid
The present investigation deals with the deformation of micropolar generalized thermoelastic solid subjected to thermo-mechanical loading due to thermal laser pulse. Laplace transform and Fourier transform techniques are used to solve the problem. Thermo-mechanical laser interactions are taken as concentrated normal force and thermal source to describe the application of approach. The closed form expressions of normal stress, tangential stress, coupled stress and temperature are obtained in the transferred domain. Numerical inversion technique of Laplace transform and Fourier transform has been implied to obtain the resulting quantities in the physical domain after developing a computer program. The normal stress, tangential stress, coupled stress and temperature are depicted graphically to show the effect of relaxation times. Some particular cases of interest are deduced from the present investigation. Keywords: Pulse Laser, Integral Transform, Thermoelastic, Boundary value Problem
Ethanopharmacology of Myrica esculenta: A Systemic Review
This systematic review focuses on Myrica Esculenta, a medicinal plant with a rich history in traditional medicine. The aim of the review is to provide a comprehensive overview of the ethnopharmacology of the plant, including its traditional uses, phytochemistry and pharmacological benefits. Common uses of M. Esculenta include treating respiratory diseases such as asthma and bronchitis, as well as gastrointestinal problems such as diarrhea and ulcers. The plant is also used to treat fever, anemia and various ear, nose and throat diseases. With its recognition in the Ayurvedic Pharmacopoeia and its widespread use in folk medicine, M. Esculenta has significant ethnopharmacological value. Through phytochemical analysis, flavonoids, tannins, steroids and terpenes have been identified as the plant\u27s main components, which are believed to contribute to its medicinal properties such as analgesic, anti-inflammatory, antioxidant and anti-cancer effects. Pharmacological studies have confirmed the therapeutic potential of M. Esculenta and demonstrated its antiasthmatic, antiulcerative, anxiolytic, hepatoprotective and wound healing properties. Conservation measures are crucial to protect the plant from over-exploitation and habitat loss. Suggestions such as micropropagation, germplasm preservation and synthetic seed production make sense for sustainable use
Deep Learning Based Place Recognition for Challenging Environments
Visual based place recognition involves recognising familiar locations despite changes in
environment or view-point of the camera(s) at the locations. There are existing methods
that deal with these seasonal changes or view-point changes separately, but few methods
exist that deal with these kind of changes simultaneously. Such robust place recognition
systems are essential to long term localization and autonomy. Such systems should be
able to deal both with conditional and viewpoint changes simultaneously. In recent times
Convolutional Neural Networks (CNNs) have shown to outperform other state-of-the art
method in task related to classi cation and recognition including place recognition. In this
thesis, we present a deep learning based planar omni-directional place recognition approach
that can deal with conditional and viewpoint variations together. The proposed method
is able to deal with large viewpoint changes, where current methods fail. We evaluate the
proposed method on two real world datasets dealing with four di erent seasons through out
the year along with illumination changes and changes occurred in the environment across
a period of 1 year respectively. We provide both quantitative (recall at 100% precision)
and qualitative (confusion matrices) comparison of the basic pipeline for place recognition
for the omni-directional approach with single-view and side-view camera approaches. The
proposed approach is also shown to work very well across di erent seasons. The results
prove the e cacy of the proposed method over the single-view and side-view cameras
in dealing with conditional and large viewpoint changes in di erent conditions including
illumination, weather, structural changes etc
Class Based Strategies for Understanding Neural Networks
One of the main challenges for broad adoption of deep learning based models such as Convolutional Neural Networks (CNN), is the lack of understanding of their decisions. In many applications, a simpler, less capable model that can be easily understood is favorable to a black-box model that has superior performance. Hence, it is paramount to have a mechanism for deep learning models such as deep neural networks to explain their decisions.
To resolve this explainability issue, in this thesis the main goal is to explore and develop new
class-enhanced support strategies for visualizing and understanding the decision-making process of deep neural networks. In particular, we take a three level approach to provide a holistic framework for explaining deep neural networks predictions.
In the first stage (Chapter 3), we first try to answer the question: based on what information neural networks make their decision and how it relates to a human expert's domain knowledge? To this end, we propose to introduce attentive response maps. The attentive response maps are able to show: 1) The locations in the input image that are contributing to decision-making and 2) the level of dominance of such locations. Through various experiments we elaborate how through attention response maps, we are able to visualize the decision-making process of deep neural networks and show where the neural networks were able to or failed to use landmark features similar to a human expert's domain knowledge.
In second stage (Chapter 4), we propose a novel end-to-end design architecture for obtaining end-to-end explanations through attentive response maps. Towards the end of this stage, we explore some of the shortcomings of the attentive response maps in failing to explain some of the complex scenarios.
In the last stage, (Chapter 5), we try to overcome the shortcomings of the binary attention maps introduced in the first stage. Towards this goal, a CLass-Enhanced Attentive Response (CLEAR) approach was introduced to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input based on spatial support. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of attention response maps-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs.
In the last Chapter of this thesis (Chapter 6), we draw conclusions about the introduced class based explanation strategies and discuss some interesting future directions, including a formulation for class based global explanation that can be used for discovering and explaining the concepts identified by trained deep neural networks using human attribute priors
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