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.

[EX Q]: This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data. We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning. Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead. Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences. We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw performance.
[EX A]: This work introduces a novel loss function for the robust training of temporal localization DNN in the presence of misaligned labels.

[EX Q]: We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition. MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by optimizing a self-supervised ranking loss. The self-supervised loss only uses data augmentation and thus does not require additional labels. Remarkably, the unified embeddings are not only much more compact than using several specialized embeddings, but they also have the same or better accuracy. When fed to a linear classifier, MultiGrain using ResNet-50 achieves 79.4% top-1 accuracy on ImageNet, a +1.8% absolute improvement over the the current state-of-the-art AutoAugment method. The same embeddings perform on par with state-of-the-art instance retrieval with images of moderate resolution. An ablation study shows that our approach benefits from the self-supervision, the pooling method and the mini-batches with repeated augmentations of the same image.

[EX A]: Combining classification and image retrieval in a neural network architecture, we obtain an improvement for both tasks.

[EX Q]: Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning. A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom. Existing methods that aim to control the dynamics of such systems are typically limited to relatively short time frames or a small number of interaction parameters. We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames. We propose to split the problem into two distinct tasks: planning and control. To this end, we introduce a predictor network that plans optimal trajectories and a control network that infers the corresponding control parameters. Both stages are trained end-to-end using a differentiable PDE solver. We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs such as the incompressible Navier-Stokes equations.
[EX A]:
We train a combination of neural networks to predict optimal trajectories for complex physical systems.