16 research outputs found
Deep Learning of Representations: Looking Forward
Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges
Neural network performance enhancement for limited nuclear fusion experiment observations supported by simulations
Adversarial examples detection through the sensitivity in space mappings
Adversarial examples (AEs) against deep neural networks (DNNs) raise wide concerns about the robustness of DNNs. Existing detection mechanisms are often limited to a given attack algorithm. Therefore, it is highly desirable to develop a robust detection approach that remains effective for a large group of attack algorithms. In addition, most of the existing defences only perform well for small images (e.g. MNIST and Canadian institute for advanced research (CIFAR)) rather than large images (e.g. ImageNet). In this paper, the authors propose a robust and effective defence method for analysing the sensitivity of various AEs, especially in a much harder case (large images). Their method first creates a feature map from the input space to the new feature space, by utilising 19 different feature mapping methods. Then, a detector is learned with the machineâlearning algorithm to recognise the unique distribution of AEs. Their extensive evaluations on their proposed detector show that their detector can achieve: (i) low falseâpositive rate (<1%), (ii) high trueâpositive rate (higher than 98%), (iii) low overhead (<0.1â
s per input), and (iv) good robustness (work well across different learning models, attack algorithms, and parameters), which demonstrate the efficacy of the proposed detector in practise