Deep neural networksGeneralization boundsLipschitz continuityNatural learningRademacher complexityRecent studies have shown that many machine learning models are vulnerable to adversarial attacks. Much remains
Deep learning 的 generalization 是目前一个非常火热的问题。众所周知深度学习不同于其他机器学习的模型，...
A. Generalization bounds for deep learning. Preprint at https://arxiv.org/abs/2012.04115 (2020). Cristianini, N., Shawe-Taylor, J., Elisseeff, A. & Kandol A. J. On kernel-target alignment. In Advances in Neural Information Processing Systems Vol. 14 (eds Dietterich, T., Becker, S....
char))task['val'].append((val_sample,char))tasks.append(task)returntasksforepochinrange(num_epochs):tasks=sample_tasks(batch_size)# 从任务分布中采样一批任务meta_loss=0fortaskintasks:# 任务内训练train_data=torch.stack([sample[0]forsampleintask['train']])train_labels=torch.tensor([sample...
(OOD) Input Domain.We examine an input domain with larger platforms compared to those utilized during training. Specifically, we extend the range of thexcoordinate in the input vectors to cover [-10, 10]. The bounds for the other inputs remain the same as during training. For additional de...
This depends on the way the bounds are derived. However, this is not of major practical importance, since the essence of the theory remains the same. Due to the importance of the VC dimension, efforts have been made to compute it for certain classes of networks. In [Baum 89] it has ...
Journal of Machine Learning Research, 12(Jul):2121–2159, 2011. 5 [17] Gintare Karolina Dziugaite and Daniel M Roy. Computing nonvacuous generalization bounds for deep (stochastic) neu- ral networks with many more parameters than training data. ...
The previous three bounds indicate that the error performance is controlled by both Ns and γ. In practice, one may end up, for example, with a large number of support vectors and at the same time with a large margin. In such a case, the error performance could be assessed, with high...
In this paper, we use tools from rate-distortion theory to establish newupper bounds on the generalization error of statistical distributed learningalgorithms. Specifically, there are $K$ clients whose individually chosenmodels are aggregated by a central server. The bounds depend on thecompressibility...
Extensions to transfer learning are developed in terms of the mismatch between training & validation distributions. (2) We establish generalization bounds for NAS problems with an emphasis on an activation search problem. When optimized with gradient-descent, we show that the train-validation procedure...