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

Children's Understanding of Time in Picture Books

Picture books are important for early literacy and convey information using both pictures and text. This study examines children’s understanding of pictorial and textual means for sequencing events in picture books, focusing specifically on ongoingness and boundedness. Ongoingness is signaled in text with imperfective aspect (English Verb+ING, was climbing) and pictorially through Moment-to-Moment picture transitions, in which a picture sequence shows stages of one event and little time passes. Boundedness is signaled in text with perfective aspect (English Verb+ED, climbed) and pictorially through Action-to-Action transitions in which pictures shift from one event to succeeding events. In this study, children (N=51; mean age=5.8 years) were read four stories modified to show a particular combination of text (imperfective or perfective aspect) and picture (Moment-to-Moment or Action-to-Action transitions) temporal markers. After reading each story participants selected “what would happen next” from four choices: continuation of current event, closure of current event, reasonable next event, or completely unrelated action. Participants also retold the stories and judged the duration of story events. Control tasks assessed knowledge of imperfective and perfective aspect and ability to order pictures in Moment-to-Moment and Action-to-Action sequences. Preliminary analyses revealed that female participants’ duration judgments were significantly shorter for stories with Moment-to-Moment picture transitions (F (1, 23) = 8.68, p = .007), suggesting that Moment-to-Moment stories were interpreted as reflecting fewer events. Although females’ results were consistent with expected interpretations, the effects of pictures and text on males’ duration judgments were non-significant. Further, males’ responses to “what would happen next” were more often completely unrelated to story events (21.9% of responses) than females’ (5.6% of responses). These results suggest that males at this age may have more difficulty than females using pictures and language to sensibly interpret narrative. Future analyses will consider children’s story retellings as well as results from adult participants to further deconstruct evident gender differences. These patterns in narrative comprehension will contribute to a greater understanding of children’s language development and of the apparent gender differences at preschool age.Ohio State University College of Arts and Sciences Research ScholarshipOhio State University Social and Behavioral Sciences Research GrantOhio State University Undergraduate Research Office Summer FellowshipNo embargoAcademic Major: Neuroscienc

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

High-temperature QCD and the classical Boltzmann equation in curved spacetime

It has been shown that the high-temperature limit of perturbative thermal QCD is easily obtained from the Boltzmann transport equation for `classical' coloured particles. We generalize this treatment to curved space-time. We are thus able to construct the effective stress-energy tensor. We give a construction for an effective action. As an example of the convenience of the Boltzmann method, we derive the high-temperature 3-graviton function. We discuss the static case.Comment: uuencoded gz-compressed .dvi fil

On Non-Abelian Structure From Matrix Coordinates

We consider the matrix quantum mechanics of N D0-branes in the background of the 1-form RR field. It is observed that the transformations of matrix coordinates of D0-branes induce on the Abelian RR field symmetry transformations that are like those of non-Abelian gauge fields. The Lorentz-like equations of motion for matrix coordinates are derived. The field strengths appearing in the Lorentz-like equations transform in the adjoint representation of U(N) under symmetry transformations. A possible relation between D0-brane dynamics in RR background, and the semi-classical dynamics of charged particles in Yang-Mills background is mentioned.Comment: 12 pages, no figures, LaTeX. v2: typos fixed, to appear in Phys. Lett.