Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Deep Learning (DL) techniques for Natural Language Processing have been evolving
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Generative flows and diffusion models have been predominantly trained on ordinal data, for
example natural images. This paper introduces two extensions of flows and diffusion for …

Time series forecasting is an important problem across many domains, including predictions
of solar plant energy output, electricity consumption, and traffic jam situation. In this paper …

Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

For most deep learning practitioners, sequence modeling is synonymous with recurrent
networks. Yet recent results indicate that convolutional architectures can outperform …

Recurrent neural networks (RNNs) have been widely used for processing sequential data.
However, RNNs are commonly difficult to train due to the well-known gradient vanishing and …

Batch normalization (BN) is a technique to normalize activations in intermediate layers of
deep neural networks. Its tendency to improve accuracy and speed up training have …