GX Yuan, CH Ho, CJ Lin - Proceedings of the IEEE, 2012 - ieeexplore.ieee.org
Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (ie, testing accuracy) of linear classifiers has …
This paper presents the results of the WMT17 shared tasks, which included three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …
Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples—a visually indistinguishable adversarial image can easily be crafted …
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices. However, the existing pruning methods are …
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. When using fine-tuning, the underlying assumption is that …
We consider a family of algorithms that successively sample and minimize simple stochastic models of the objective function. We show that under reasonable conditions on …
To attain a favorable performance on large-scale datasets, convolutional neural networks (CNNs) are usually designed to have very high capacity involving millions of parameters. In …
L Xiao, T Zhang - SIAM Journal on Optimization, 2014 - SIAM
We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth component functions, and the other is a general convex …
J Duchi, E Hazan, Y Singer - Journal of machine learning research, 2011 - jmlr.org
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient …