Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …

This paper provides an entry point to the problem of interpreting a deep neural network
model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a …

While deep learning has enabled tremendous progress on text and image datasets, its
superiority on tabular data is not clear. We contribute extensive benchmarks of standard and …

There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

A key element in solving real-life data science problems is selecting the types of models to
use. Tree ensemble models (such as XGBoost) are usually recommended for classification …

Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

The existing literature on deep learning for tabular data proposes a wide range of novel
architectures and reports competitive results on various datasets. However, the proposed …

We propose $\textit {Mish} $, a novel self-regularized non-monotonic activation function
which can be mathematically defined as: $ f (x)= x\tanh (softplus (x)) $. As activation …

The choice of activation functions in deep networks has a significant effect on the training
dynamics and task performance. Currently, the most successful and widely-used activation …

Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …