TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models. Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations. 
 Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance. Effective approaches for such problems would require the ability to recognize the plans of agents from video information. Traditional plan recognition algorithms rely on access to detailed planning domain models. One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences. Such plan recognition approaches expect observed action sequences as inputs. However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions. In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.

SOLUTION: Handling Uncertainty in Visual Perception for Plan Recognition

PROBLEM: We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay. In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam/AdamW. 
 Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam.

SOLUTION: NovoGrad -  an adaptive SGD method with layer-wise gradient normalization and decoupled weight decay. 

PROBLEM: We show that gradient descent on an unregularized logistic regression
 problem, for almost all separable datasets, converges to the same direction as the max-margin solution. The result generalizes also to other monotone decreasing loss functions with an infimum at infinity, and we also discuss a multi-class generalizations to the cross entropy loss. Furthermore,
 we show this convergence is very slow, and only logarithmic in the
 convergence of the loss itself. This can help explain the benefit
 of continuing to optimize the logistic or cross-entropy loss even
 after the training error is zero and the training loss is extremely
 small, and, as we show, even if the validation loss increases. Our
 methodology can also aid in understanding implicit regularization
 in more complex models and with other optimization methods.

SOLUTION:
The normalized solution of gradient descent on logistic regression (or a similarly decaying loss) slowly converges to the L2 max margin solution on separable data.