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

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

Effect of biofertilizers on horticultural and yield traits in french bean var. Contender under dry temperate conditions of Kinnaur district of Himachal Pradesh

Kinnaur district is known as the dry temperate zone of Himachal Pradesh and is known for off season and quality production of vegetables.In this district of Himachal Pradesh, Natural farming is mostly done with the minimum use of chemical fertilizers. Farmers are unaware of the judicious use of farm yard manure, and biofertilizers due to which yield of the french bean is very low (50-70 q/ha). French bean is one of the most important vegetables intercropped with apple in Kinnaur District. An experiment was conducted during the summer season of 2011 at the Experimental Farm of Vegetable Research Station, Kalpa, Kinnaur, Himachal Pradesh to study the effect of Rhizobium and Phosphorus Solublizing Bacteria (PSB) on the horticultural and yield traits in french bean var. Contender. Six treatments comprising seed treatments (with and without Rhizobium), seed treatment (with and without PSB) along with the combination of 60 % dose of recommended quantity of Calcium Ammonium Nitrate and 75 % dose of recommended quantity of Single Super Phosphate and organic matter were evaluated in a Randomized Complete Block Design (RCBD) with three replications. The results revealed that T5 treatment, i.e. Rhizobium+ PSB+ Organic matter resulted in more number of pods per plant (20), pod length (18 cm) and pod yield/ha (140 q/ha)