robust learning
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2022 ◽  
Vol 4 ◽  
Author(s):  
Gauri Jagatap ◽  
Ameya Joshi ◽  
Animesh Basak Chowdhury ◽  
Siddharth Garg ◽  
Chinmay Hegde

In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additional entropic regularization. Our loss function considers the contribution of adversarial samples that are drawn from a specially designed distribution in the data space that assigns high probability to points with high loss and in the immediate neighborhood of training samples. Our proposed algorithms optimize this loss to seek adversarially robust valleys of the loss landscape. Our approach achieves competitive (or better) performance in terms of robust classification accuracy as compared to several state-of-the-art robust learning approaches on benchmark datasets such as MNIST and CIFAR-10.


IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Xin Wu ◽  
Qing Liu ◽  
Jiarui Qin ◽  
Yong Yu

2022 ◽  
pp. 91-107
Author(s):  
David E. Pines ◽  
Natalia Bernal Restrepo

The authors demonstrate through specific case studies, representative of Civil Society in Least Developed Countries (LDCs), how user-acquired knowledge has the potential to impact both economic growth and economic development. In the interconnected, interdependent 21st century world of full participation as envisioned in UN Agenda 2030, it is essential to equip the people of developing nations with the tools to participate, grow, and develop themselves. This chapter both illustrates the importance of education and lifelong learning as well as highlighting the potential of a robust learning experience platform in geographies in which issues of infrastructure, connectivity, and access are some of the greatest challenges to overcome.


2021 ◽  
Author(s):  
Yifan Feng ◽  
René Caldentey ◽  
Christopher Thomas Ryan

When companies develop new products, there are often competing designs from which to choose to take to market. How to decide? Traditional methods, such as focus groups, do not scale to the modern marketplace in which tastes evolve rapidly. In “Robust Learning of Consumer Preferences,” Feng, Caldentey, and Ryan develop a data-driven approach to deciding which design to produce by displaying a sequence of subsets of possible designs to potential customers. Their framework finds solutions that are robust to any model of consumer choice within a broad family that includes common choice models studied in the literature as special cases. Previous research focuses on algorithms whose performances are tied to a given choice model. Their algorithm is shown to be asymptotically optimal in a worst-case sense. The promising practical performance of the algorithm is demonstrated through a comprehensive numerical study using real data.


2021 ◽  
Author(s):  
Prathyush Poduval ◽  
Zhuowen Zou ◽  
Hassan Najafi ◽  
Houman Homayoun ◽  
Mohsen Imani
Keyword(s):  
Raw Data ◽  

2021 ◽  
pp. 108467
Author(s):  
Haoliang Sun ◽  
Chenhui Guo ◽  
Qi Wei ◽  
Zhongyi Han ◽  
Yilong Yin
Keyword(s):  

Author(s):  
Sai Gokul Hariharan ◽  
Christian Kaethner ◽  
Norbert Strobel ◽  
Markus Kowarschik ◽  
Rebecca Fahrig ◽  
...  

Abstract Purpose: Since guidance based on X-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the X-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures. Method: We present a robust learning-based denoising strategy involving model- based simulations of low-dose X-ray images during the training phase. The method also utilizes a data-driven normalization step - based on an X-ray imaging model - to stabilize the mixed signal-dependent noise associated with X-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose X-ray images on the denoising results. Results: A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band co efficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy. Conclusion: The proposed learning-based denoising strategy provides scope for significant X-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training ph


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicolas Perez-Nieves ◽  
Vincent C. H. Leung ◽  
Pier Luigi Dragotti ◽  
Dan F. M. Goodman

AbstractThe brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.


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