Day 3 (7/10/19): Experimentation with Classifiers

Today I took a step forward toward a more specific focus on machine learning and neural networks. After quickly reviewing yesterday's material through Datacamp exercises on python and Numpy, I spent most of the morning completing tutorials on implementing various classifiers using the machine learning software Scikit-learn.

First using the simple K Nearest Neighbors Classifier, I learned how to fit classifiers to data, use the classifiers to predict targets for new test data, and evaluate the accuracy and errors of each classifier using Confusion Matrices and Classification Reports. Then, I applied what I learned to both the Iris (classifying three types of flowers) and MNIST (classifying handwritten digits) datasets implementing different types of models such as Support Vector Machine (both Linear and RBF), Gaussian Process, and Multilayer Perceptron Classifiers.

In the afternoon, I watched a Youtube playlist which provided an introduction to neural networks including a basic Python implementation of Linear Regression. After finishing that simple neural network, I started learning more specifically about Convolutional Neural Networks (CNNs), especially used in computer vision problems. With guidance from resources such as Stanford's CS231 and RIT's Deep Learning for Vision courses, I hope to be able to implement CNNs in code later this week.

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Day 30 (8/16/19): Final Presentations

Today we gave our final presentations, and everyone did a great job. I would like to thank everyone who helped me with this amazing experience! I'm very thankful to have had the opportunity to work on such interesting research with such amazing people this summer.

Day 22 (8/6/19): Streaming Linear Discriminant Analysis

Today I tested the previously trained models using the Stanford dogs dataset as the inter dataset evaluator for OOD instead of the Oxford flowers dataset. However, as expected, the omega values for performance were pretty much the same as before and didn't make much of a difference as the datasets varied. I also implemented a streaming linear discriminant analysis model (SLDA) which differed from the previous incrementally trained models. This model didn't perform as well in terms of accuracy however as only the last layer of the model was trained and streaming is more of a difficult task. Nevertheless, we did show that Mahalanobis can be used in a streaming paradigm to recover some OOD performance in an online setting. This is likely to be a large focus of my presentation as it has never been discussed prior. Tomorrow, I plan to implement an L2SP model with elastic weight consolidation as well as iCarl to serve as two more baselines to compare our experiments to.

Day 24 (8/8/19): Multilayer Perceptron Experiment

I continued gathering more results for my presentation today, and the data table is coming along nicely. We are able to see a significant trend that using Mahalanobis instead of Baseline Thresholding recovers much of the OOD recognition that is lost with streaming or incremental models. The SLDA model appears to be a lightweight, accurate streaming model which can be paired with Mahalanobis to be useful as an embedded agent in the real world. For the purposes of demonstrating catastrophic forgetting, I ran five experiments and averaged the results for a simple incrementally trained MLP. Obviously, the model failed miserably and was achieving only about 1% of the accuracy of the offline model. Including this is only to show how other forms of streaming and incremental models are necessary to develop lifelong learning agents. A diagram of a simple multilayer perceptron.

RIT CIS Summer Internship

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