137 citations · 170 across the 4 of their papers we have counts for
6 papers · 1 filter
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach +1
In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function ba…
MetaPoison: Practical General-purpose Clean-label Data Poisoning
W. Ronny Huang, Jonas Geiping, Liam Fowl +2
Data poisoning -- the process by which an attacker takes control of a model by making imperceptible changes to a subset of the training data -- is an emerging threat in the context…
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach +1
Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained set…
Adversarial Training for Free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi +6
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, t…
Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor, Ryan Burmeister, Zheng Xu +3
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks. This is largely because conven…
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs
Gavin Taylor, Ron Parr
Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear Programming (RALP) could produce value functions and policies which compared favorably to established lin…