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.

Example Input: In this work, we focus on the problem of grounding language by training an agent
 to follow a set of natural language instructions and navigate to a target object
 in a 2D grid environment. The agent receives visual information through raw
 pixels and a natural language instruction telling what task needs to be achieved.
 Other than these two sources of information, our model does not have any prior
 information of both the visual and textual modalities and is end-to-end trainable.
 We develop an attention mechanism for multi-modal fusion of visual and textual
 modalities that allows the agent to learn to complete the navigation tasks and also
 achieve language grounding. Our experimental results show that our attention
 mechanism outperforms the existing multi-modal fusion mechanisms proposed in
 order to solve the above mentioned navigation task. We demonstrate through the
 visualization of attention weights that our model learns to correlate attributes of
 the object referred in the instruction with visual representations and also show
 that the learnt textual representations are semantically meaningful as they follow
 vector arithmetic and are also consistent enough to induce translation between instructions
 in different natural languages. We also show that our model generalizes
 effectively to unseen scenarios and exhibit zero-shot generalization capabilities.
 In order to simulate the above described challenges, we introduce a new 2D environment
 for an agent to jointly learn visual and textual modalities
Example Output: Attention based architecture for language grounding via reinforcement learning in a new customizable 2D grid environment  

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

Example Input: In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves most nonlinear functions and excludes piecewise linear functions), we have that arbitrary first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular. We essentially show that these non-singular hidden layer matrix satisfy a ``"good" property for these big class of activation functions. Techniques involved in proving this result inspire us to look at a new algorithmic, where in between two gradient step of hidden layer, we add a stochastic gradient descent (SGD) step of the output layer. In this new algorithmic framework, we extend our earlier result and show that for all finite iterations the hidden layer satisfies the``good" property mentioned earlier therefore partially explaining success of noisy gradient methods and addressing the issue of data independency of our earlier result. Both of these results are easily extended to hidden layers given by a flat matrix from that of a square matrix. Results are applicable even if network has more than one hidden layer provided all inner hidden layers are arbitrary, satisfy non-singularity, all activations are from the given class of differentiable functions and optimization is only with respect to the outermost hidden layer. Separately, we also study the smoothness properties of the objective function and show that it is actually Lipschitz smooth, i.e., its gradients do not change sharply. We use smoothness properties to guarantee asymptotic convergence of $O(1/\text{number of iterations})$ to a first-order optimal solution.
Example Output:
This paper talks about theoretical properties of first-order optimal point of two layer neural network in over-parametrized case