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.

Input: Consider Input: We consider the problem of unsupervised learning of a low dimensional, interpretable, latent state of a video containing a moving object. The problem of distilling dynamics from pixels has been extensively considered through the lens of graphical/state space models that exploit Markov structure for cheap computation and structured graphical model priors for enforcing interpretability on latent representations. We take a step towards extending these approaches by discarding the Markov structure; instead, repurposing the recently proposed Gaussian Process Prior Variational Autoencoder for learning sophisticated latent trajectories. We describe the model and perform experiments on a synthetic dataset and see that the model reliably reconstructs smooth dynamics exhibiting U-turns and loops. We also observe that this model may be trained without any beta-annealing or freeze-thaw of training parameters. Training is performed purely end-to-end on the unmodified evidence lower bound objective. This is in contrast to previous works, albeit for slightly different use cases, where application specific training tricks are often required.

Output: We learn sohpisticated trajectories of an object purely from pixels with a toy video dataset by using a VAE structure with a Gaussian process prior.


Input: Consider Input: Recurrent Neural Networks architectures excel at processing sequences by
 modelling dependencies over different timescales. The recently introduced
 Recurrent Weighted Average (RWA) unit captures long term dependencies
 far better than an LSTM on several challenging tasks. The RWA achieves
 this by applying attention to each input and computing a weighted average
 over the full history of its computations. Unfortunately, the RWA cannot
 change the attention it has assigned to previous timesteps, and so struggles
 with carrying out consecutive tasks or tasks with changing requirements.
 We present the Recurrent Discounted Attention (RDA) unit that builds on
 the RWA by additionally allowing the discounting of the past.
 We empirically compare our model to RWA, LSTM and GRU units on
 several challenging tasks. On tasks with a single output the RWA, RDA and
 GRU units learn much quicker than the LSTM and with better performance.
 On the multiple sequence copy task our RDA unit learns the task three
 times as quickly as the LSTM or GRU units while the RWA fails to learn at
 all. On the Wikipedia character prediction task the LSTM performs best
 but it followed closely by our RDA unit. Overall our RDA unit performs
 well and is sample efficient on a large variety of sequence tasks.

Output: We introduce the Recurrent Discounted Unit which applies attention to any length sequence in linear time


Input: Consider Input: Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization.   The expense of producing these examples during training often precludes adversarial training from use on complex image datasets. 
 In this study, we explore the mechanisms by which adversarial training improves classifier robustness, and show that these mechanisms can be effectively mimicked using simple regularization methods, including label smoothing and logit squeezing.  
 Remarkably, using these simple regularization methods in combination with Gaussian noise injection, we are able to achieve strong adversarial robustness -- often exceeding that of adversarial training -- using no adversarial examples.


Output: Achieving strong adversarial robustness comparable to adversarial training without training on adversarial examples