Joseph O'Sullivan

ICML Conference 2000 Conference Paper

FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness

• Joseph O'Sullivan
• John Langford 0001
• Rich Caruana
• Avrim Blum

IROS Conference 1998 Conference Paper

Multitask pattern recognition for autonomous robots

• Rich Caruana
• Joseph O'Sullivan

We present a method called multitask pattern recognition (MTPR) that improves the accuracy and robustness of neural net based vision systems. The method trains neural nets on auxiliary recognition problems at the same time as the nets are trained on the main recognition task. The predictions the system makes for the auxiliary recognition tasks are not used, but the internal features learned for the auxiliary tasks improve performance on the main task. In addition, the auxiliary tasks allow us to focus the attention of learning towards image features it would otherwise ignore, thereby increasing the robustness of the learned models. We demonstrate MTPR on three problems (one synthetic, two real). On these problems MTPR improves performance 10%-30%. MTPR is applicable to many pattern recognition problems.

ICML Conference 1996 Conference Paper

Discovering Structure in Multiple Learning Tasks: The TC Algorithm

• Sebastian Thrun
• Joseph O'Sullivan