730 research outputs found
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
We investigate the generalizability of deep learning based on the sensitivity
to input perturbation. We hypothesize that the high sensitivity to the
perturbation of data degrades the performance on it. To reduce the sensitivity
to perturbation, we propose a simple and effective regularization method,
referred to as spectral norm regularization, which penalizes the high spectral
norm of weight matrices in neural networks. We provide supportive evidence for
the abovementioned hypothesis by experimentally confirming that the models
trained using spectral norm regularization exhibit better generalizability than
other baseline methods
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning
DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement
learning using deep neural networks. DQNs require a large buffer and batch
processing for an experience replay and rely on a backpropagation based
iterative optimization, making them difficult to be implemented on
resource-limited edge devices. In this paper, we propose a lightweight
on-device reinforcement learning approach for low-cost FPGA devices. It
exploits a recently proposed neural-network based on-device learning approach
that does not rely on the backpropagation method but uses OS-ELM (Online
Sequential Extreme Learning Machine) based training algorithm. In addition, we
propose a combination of L2 regularization and spectral normalization for the
on-device reinforcement learning so that output values of the neural network
can be fit into a certain range and the reinforcement learning becomes stable.
The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a
low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate
that the proposed algorithm and its FPGA implementation complete a CartPole-v0
task 29.77x and 89.40x faster than a conventional DQN-based approach when the
number of hidden-layer nodes is 64
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
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