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 Input:
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 Output:
This work introduces a novel loss function for the robust training of temporal localization DNN in the presence of misaligned labels.


Ex Input:
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as  offensive basketball gameplay.   When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables.   Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulable way. We present a hierarchical framework that can effectively learn such sequential generative models.   Our approach is inspired by recent work on leveraging programmatically produced weak labels, which we extend to the spatiotemporal regime. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts.

Ex Output:
We blend deep generative models with programmatic weak supervision to generate coordinated multi-agent trajectories of significantly higher quality than previous baselines.


Ex Input:
We propose a novel yet simple neural network architecture for topic modelling. The method is based on training an autoencoder structure where the bottleneck represents the space of the topics distribution and the decoder outputs represent the space of the words distributions over the topics. We exploit an auxiliary decoder to prevent mode collapsing in our model.   A key feature for an effective topic modelling method is having sparse topics and words distributions, where there is a trade-off between the sparsity level of topics and words. This feature is implemented in our model by L-2 regularization and the model hyperparameters take care of the trade-off.   We show in our experiments that our model achieves competitive results compared to the state-of-the-art deep models for topic modelling, despite its simple architecture and training procedure. The “New York Times” and “20 Newsgroups” datasets are used in the experiments.



Ex Output:

A deep model for topic modelling