An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated

The Advocate, November 10, 2011

https://red.mnstate.edu/advocate/1269/thumbnail.jp

A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning

Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others

Choose an Eye

Senior Project submitted to The Division of Arts of Bard College

A complementing approach for identifying ethical issues in care robotics – grounding ethics in practical use

We use a long-term study of a robotic eating-aid for disabled users to illustrate how empirical use give rise to a set of ethical issues that might be overlooked in ethic discussions based on theoretical extrapolation of the current state-of-the-art in robotics. This approach provides an important complement to the existing robot ethics by revealing new issues as well as providing actionable guidance for current and future robot design. We discuss our material in relation to the literature on robot ethics, specifically the risk of robots performing care taking tasks and thus causing increased isolation for care recipients. Our data identifies a different set of ethical issues such as independence, privacy, and identity where robotics, if carefully designed and developed, can make positive contributions

Adversarial Multi-task Learning for Text Classification

Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at \url{http://nlp.fudan.edu.cn/data/}Comment: Accepted by ACL201