3 accepted papers
Learning representations in an unsupervised or self-supervised manner is a growing area of research. Current approaches in representation learning seek to maximize the mutual information between the learned representation and original data. One of the most popular ways to estimate mutual informatio…
Many popular first-order optimization methods accelerate the convergence rate of deep learning models. However, these algorithms require auxiliary variables, which cost additional memory proportional to the number of parameters in the model. The problem is becoming more severe as models grow larger…
Feature selection is an important challenge in machine learning. It plays a crucial role in the explainability of machine-driven decisions that are rapidly permeating throughout modern society. Unfortunately, the explosion in the size and dimensionality of real-world datasets poses a severe challeng…