Han, Vardan Papyan, and David Donoho won an outstanding paper award at ICLR 2022 for their paper Neural collapse under MSE loss: proximity to and dynamics on the central path. Here, they tell us more about this research, their methodology, and what the implications of this work are. Our work takes a data scientific approach to understanding deep neural networks. We make scientific measurements that identify common, prevalent empirical phenomena that occur in canonical deep classification networks trained with paradigmatic methods. We then build and analyze a mathematical model to understand the phenomena.

Cancer is still one of the most devastating diseases of our time. One way of automatically classifying tumor samples is by analyzing its derived molecular information (i.e., its genes expression signatures). In this work, we aim to distinguish three different types of cancer: thyroid, skin, and stomach. For that, we compare the performance of a Denoising Autoencoder (DAE) used as weight initialization of a deep neural network. Although we address a different domain problem in this work, we have adopted the same methodology of Ferreira et al.. In our experiments, we assess two different approaches when training the classification model: (a) fixing the weights, after pre-training the DAE, and (b) allowing fine-tuning of the entire classification network. Additionally, we apply two different strategies for embedding the DAE into the classification network: (1) by only importing the encoding layers, and (2) by inserting the complete autoencoder. Our best result was the combination of unsupervised feature learning through a DAE, followed by its full import into the classification network, and subsequent fine-tuning through supervised training, achieving an F1 score of 98.04% +/- 1.09 when identifying cancerous thyroid samples.