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The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations. 

Line charts are often used to convey high level information about time series data. Unfortunately, these charts are not always described in text, and as a result are often inaccessible to users with visual impairments who rely on screen readers. In these situations, an automated system that can describe the overall trend in a chart would be desirable. This paper presents a novel approach to classifying trends in line chart images, for use in existing chart summarization tools. Previous projects have introduced approaches to automatically summarize line charts, but have thus far been unable to describe chart trends with sufficient accuracy for real-world applications. Instead of classifying an image’s trend via a convolutional neural network (CNN) system, as has been done previously, we present an architecture similar to bag-of-words (BoW) techniques for computer vision, mapping the image classification problem to an analogous natural language problem. We divided images into matrices of image patches which we then each treated as a series of “visual words” which were used to classify each image. We utilized natural language processing (NLP) word embeddings techniques to to create embeddings of visual words that allowed us to model contextual similarity between patches. We trained a linear support vector machine (SVM) model using these patch embeddings as inputs to classify the chart trend. We compared this method against a ResNet classifier pre-trained on ImageNet. Our experimental results showed that the novel approach presented in this paper outperforms existing approaches. 

The state-of-the-art in machine learning has been achieved primarily by deep learning artificial neural networks. These networks are powerful but biologically implausible and energy intensive. In parallel, a new paradigm of neural network is being researched that can alleviate some of the computational and energy issues. These networks, spiking neural networks (SNNs), have transformative potential if the community is able to bridge the gap between deep learning and SNNs. However, SNNs are notoriously difficult to train and lack precision in their communication. In an effort to overcome these limitations and retain the benefits of the learning process in deep learning, we investigate novel ways to translate between them. We construct several network designs with varying degrees of biological plausibility. We then test our designs on an image classification task and demonstrate our designs allow for a customized tradeoff between biological plausibility, power efficiency, inference time, and accuracy. 

Sparsity is a desirable property as our natural environment can be described by a small number of structural primitives. Strong evidence demonstrates that the brain’s representation is both explicit and sparse, which makes it metabolically efficient by reducing the cost of code transmission. In current standardized machine learning practices, end-to-end classification pipelines are much more prevalent. For the brain, there is no single classification objective function optimized by back-propagation. Instead, the brain is highly modular and learns based on local information and learning rules. In our work, we seek to show that an unsupervised, biologically inspired sparse coding algorithm can create a sparse representation that achieves a classification accuracy on par with standard supervised learning algorithms. We leverage the concept of multi-modality to show that we can link the embedding space with multiple, heterogeneous modalities. Furthermore, we demonstrate a sparse coding model which controls the latent space and creates a sparse disentangled representation, while maintaining a high classification accuracy. 

We present a multimodal deep learning framework that can generate summarization text supporting the main idea of an information graphic for presentation to a person who is blind or visually impaired. The framework utilizes the visual, textual, positional, and size characteristics extracted from the image to create the summary. Different and complimentary neural architectures are optimized for each task using crowdsourced training data. From our quantitative experiments and results, we explain the reasoning behind our framework and show the effectiveness of our models. Our qualitative results showcase text generated from our framework and show that Mechanical Turk participants favor them to other automatic and human generated summarizations. We describe the design and results of an experiment to evaluate the utility of our system for people who have visual impairments in the context of understanding Twitter Tweets containing line graphs. 

We present research in the modeling of neurons within Drosophila (fruit fly) olfaction. We describe the process from data collection, to model creation, and spike generation. Our approach utilizes computational elements such as spiking neural networks that employ leaky integrate-and-fire neurons with adaptive firing behavior that more closely mimick biological neurons. We describe the methods of several learning implementations in both software and hardware. Finally, we present both quantitative and qualitative results on learning spiking neural network models. 

While deep learning continues to permeate through all fields of signal processing and machine learning, a critical exploit in these frameworks exists and remains unsolved. These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus signal by an imperceptible amount. The attack takes advantage of statistical irregularities within the training data, where the added perturbations can move the image across deep learning decision boundaries. What is even more alarming is the transferability of these attacks to different deep learning models and architectures. This means a successful attack on one model has adversarial effects on other, unrelated models. In a general sense, adversarial attack through perturbations is not a machine learning vulnerability. Human and biological vision can also be fooled by various methods, i.e. mixing high and low frequency images together, by altering semantically related signals, or by sufficiently distorting the input signal. However, the amount and magnitude of such a distortion required to alter biological perception is at a much larger scale. In this work, we explored this gap through the lens of biology and neuroscience in order to understand the robustness exhibited in human perception. Our experiments show that by leveraging sparsity and modeling the biological mechanisms at a cellular level, we are able to mitigate the effect of adversarial alterations to the signal that have no perceptible meaning. Furthermore, we present and illustrate the effects of top-down functional processes that contribute to the inherent immunity in human perception in the context of exploiting these properties to make a more robust machine vision system. 

Adversarial images are a class of images that have been slightly altered by very specific noise to change the way a deep learning neural network classifies the image. In many cases, this particular noise is imperceptible to the human vision system and thus presents a vulnerability of significant concern to the machine learning and artificial intelligence community. Research towards mitigating this type of attack has taken many forms, one of which is to filter or post process the image before classifying the image with a deep neural network. Techniques such as smoothing, filtering, and compression have been used with varying levels of success. In our work, we explored the use of a neuromorphic software and hardware approach as a protection against adversarial image attack. The algorithm governing our neuromorphic approach is based upon sparse coding. Our sparse coding approach is solved using a dynamic system of equations that models biological low level vision. Our quantitative and qualitative results show that a sparse coding reconstruction is remarkably invariant to changes in sparsity and reconstruction error with respect to classification accuracy. Furthermore, our approach is able to maintain low reconstruction errors without sacrificing classification performance.