In neural networks literature, there is a strong interest in identifying and defining activation
functions which can improve neural network performance. In recent years there has been a …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Catastrophic forgetting occurs when a neural network loses the information learned in a
previous task after training on subsequent tasks. This problem remains a hurdle for artificial …

Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks
have been impeded by a chronic problem called catastrophic forgetting. Although simply …

While deep learning has led to remarkable advances across diverse applications, it
struggles in domains where the data distribution changes over the course of learning. In …

Neural networks are achieving state of the art and sometimes super-human performance on
learning tasks across a variety of domains. Whenever these problems require learning in a …

Neural networks have been successfully applied in applications with a large amount of
labeled data. However, the task of rapid generalization on new concepts with small training …

Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks,
however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To …

Catastrophic forgetting is a problem of neural networks that loses the information of the first
task after training the second task. Here, we propose a method, ie incremental moment …

Semantic segmentation research has recently witnessed rapid progress, but many leading
methods are unable to identify object instances. In this paper, we present Multi-task Network …