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.
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Question: We consider the problem of generating plausible and diverse video sequences, when we are only given a start and an end frame. This task is also known as inbetweening, and it belongs to the broader area of stochastic video generation, which is generally approached by means of recurrent neural networks (RNN). In this paper, we propose instead a fully convolutional model to generate video sequences directly in the pixel domain. We first obtain a latent video representation using a stochastic fusion mechanism that learns how to incorporate information from the start and end frames. Our model learns to produce such latent representation by progressively increasing the temporal resolution, and then decode in the spatiotemporal domain using 3D convolutions. The model is trained end-to-end by minimizing an adversarial loss. Experiments on several widely-used benchmark datasets show that it is able to generate meaningful and diverse in-between video sequences, according to both quantitative and qualitative evaluations.

Answer: This paper presents method for stochastically generating in-between video frames from given key frames, using direct 3D convolutions.

Question: Asynchronous distributed gradient descent algorithms for training of deep neural
networks are usually considered as inefficient, mainly because of the Gradient delay
problem. In this paper, we propose a novel asynchronous distributed algorithm
that tackles this limitation by well-thought-out averaging of model updates, computed
by workers. The algorithm allows computing gradients along the process
of gradient merge, thus, reducing or even completely eliminating worker idle time
due to communication overhead, which is a pitfall of existing asynchronous methods.
We provide theoretical analysis of the proposed asynchronous algorithm,
and show its regret bounds. According to our analysis, the crucial parameter for
keeping high convergence rate is the maximal discrepancy between local parameter
vectors of any pair of workers. As long as it is kept relatively small, the
convergence rate of the algorithm is shown to be the same as the one of a sequential
online learning. Furthermore, in our algorithm, this discrepancy is bounded
by an expression that involves the staleness parameter of the algorithm, and is
independent on the number of workers. This is the main differentiator between
our approach and other solutions, such as Elastic Asynchronous SGD or Downpour
SGD, in which that maximal discrepancy is bounded by an expression that
depends on the number of workers, due to gradient delay problem. To demonstrate
effectiveness of our approach, we conduct a series of experiments on image
classification task on a cluster with 4 machines, equipped with a commodity communication
switch and with a single GPU card per machine. Our experiments
show a linear scaling on 4-machine cluster without sacrificing the test accuracy,
while eliminating almost completely worker idle time. Since our method allows
using commodity communication switch, it paves a way for large scale distributed
training performed on commodity clusters.

Answer: A method for an efficient asynchronous distributed training of deep learning models along with theoretical regret bounds.

Question: We study the error landscape of deep linear and nonlinear neural networks with the squared error loss. Minimizing the loss of a deep linear neural network is a nonconvex problem, and despite recent progress, our understanding of this loss surface is still incomplete. For deep linear networks, we present necessary and sufficient conditions for a critical point of the risk function to be a global minimum. Surprisingly, our conditions provide an efficiently checkable test for global optimality, while such tests are typically intractable in nonconvex optimization. We further extend these results to deep nonlinear neural networks and prove similar sufficient conditions for global optimality, albeit in a more limited function space setting.

Answer:
We provide efficiently checkable necessary and sufficient conditions for global optimality in deep linear neural networks, with some initial extensions to nonlinear settings.