Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

In recent years a vast amount of visual content has been generated and shared from many
fields, such as social media platforms, medical imaging, and robotics. This abundance of …

Metrics for evaluating generative models aim to measure the discrepancy between real and
generated images. The oftenused Frechet Inception Distance (FID) metric, for example …

How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard
dataset such as CIFAR-10 when its “width”—namely, number of channels in convolutional …

Clustering is a class of unsupervised learning methods that has been extensively applied
and studied in computer vision. Little work has been done to adapt it to the end-to-end …

Modern convolutional networks are not shift-invariant, as small input shifts or translations
can cause drastic changes in the output. Commonly used downsampling methods, such as …

Federated learning poses new statistical and systems challenges in training machine
learning models over distributed networks of devices. In this work, we show that multi-task …

N 6-Methyladenosine (m6A) refers to methylation modification of the adenosine nucleotide
acid at the nitrogen-6 position. Many conventional computational methods for identifying N 6 …

Deep learning has proven itself as a successful set of models for learning useful semantic
representations of data. These, however, are mostly implicitly learned as part of a …

This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset
for handwritten digit recognition. This dataset has been extensively used to validate novel …