A Kernel Perspective for Regularizing Deep Neural Networks

We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it admits upper and lower approximations leading to various practical strategies. Specifically, this perspective (i) provides a common umbrella for many existing regularization principles, including spectral norm and gradient penalties, or adversarial training, (ii) leads to new effective regularization penalties, and (iii) suggests hybrid strategies combining lower and upper bounds to get better approximations of the RKHS norm. We experimentally show this approach to be effective when learning on small datasets, or to obtain adversarially robust models.Comment: ICM

Bioeconomic modeling of wetlands and waterfowl in Western Canada: Accounting for amenity values

This study extends an original bioeconomic model of optimal duck harvest and wetland retention by bringing in amenity values related to the nonmarket (in situ) benefits of waterfowl plsi the ecosystem values of wetlands themselves. The model maximizes benefits to hunters as well as the amenity values of ducks and ecosystem benefits of wetlands, subject to the population dynamics. Results indicate that wetlands and duck harvests need to be increased relative to historical levels. Further, the socially optimal ratio of duck harvest to wetlands is larger than what has been observed historically. Including amenity values leads to a significant increase in the quantity of wetlands and duck harvests relative to models that focus only on hunting values.bioeconomic modelling, wetland protection, wildlife management, nonmarket values, Prairie pothole region, Environmental Economics and Policy, Q57, C61, Q25,

Bioeconomic modeling of wetlands and waterfowl in Western Canada: Accounting for amenity values

bioeconomic modelling; wetland protection; wildlife management; nonmarket values; Prairie pothole region