📄️ MOOC Dropout Prediction
Massive Online Open Courses (MOOCs)
📄️ ResNet Animal Classification
For the image track, we implemented a Residual Neural Network (ResNet) architecture, a derivative of the basic convolutional neural network on a range of image classification tasks. This network was trained on ImageNet, an extremely common dataset for image classification, and will be used in conjunction with a dataset for tumor classification such as that of Panigrahi (2021) and Sarta (2020). Coined by He et al. (2015), ResNet addresses the issues of neural networks’ degradation, where training and evaluation losses spike and lose predictability as a model’s depth passes a certain point. The proposed solution consists of identity mapping, whereby layers pass their outputs to a mapping function that performs one of two actions: it may shortcut part of the input to be recombined at a deeper layer (discussed in Hochreiter et al., 1997), or it may utilize a nonlinear function which asymptotically approximated the use of multiple linear layers at once (introduced in Jégou et al., 2012). This development allows deep neural networks to have no worse loss than shallow ones of appropriate depth.