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: Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift.   Ideally, a model would assign lower confidence to points unlike those from the training distribution.   We propose a regularizer which addresses this issue by training with interpolated hidden states and encouraging the classifier to be less confident at these points.   Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes.   This has a major advantage in that it avoids the underfitting which can result from interpolating in the input space.   We prove that the exact condition for this problem of underfitting to be avoided by Manifold Mixup is that the dimensionality of the hidden states exceeds the number of classes, which is often the case in practice.   Additionally, this concentration can be seen as making the features in earlier layers more discriminative.   We show that despite requiring no significant additional computation, Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.
Example Output: A method for learning better representations, that acts as a regularizer and despite its no significant additional computation cost , achieves improvements over strong baselines on Supervised and Semi-supervised Learning tasks.

Example Input: Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network. While convolutional spiking neural networks have been demonstrated to achieve near state-of-the-art performance, only one solution has been proposed to convert gated recurrent neural networks, so far.
 Recurrent neural networks in the form of networks of gating memory cells have been central in state-of-the-art solutions in problem domains that involve sequence recognition or generation. Here, we design an analog gated LSTM cell where its neurons can be substituted for efficient stochastic spiking neurons. These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains. For such neurons, we approximate the effective activation function, which resembles a sigmoid. We show how analog neurons with such activation functions can be used to create an analog LSTM cell; networks of these cells can then be trained with standard backpropagation. We train these LSTM networks on a noisy and noiseless version of the original sequence prediction task from Hochreiter & Schmidhuber (1997), and also on a noisy and noiseless version of a classical working memory reinforcement learning task, the T-Maze. Substituting the analog neurons for corresponding adaptive spiking neurons, we then show that almost all resulting spiking neural network equivalents correctly compute the original tasks.
Example Output:  We demonstrate a gated recurrent asynchronous spiking neural network that corresponds to an LSTM unit.

Example Input: We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute
 editing workflow. Current face modeling methods using 3DMM suffer from the lack of local control. We thus create a 3DMM by
 combining local part-based 3DMM for the eyes, nose, mouth, ears, and facial mask regions. Our local PCA-based approach
 uses a novel method to select the best eigenvectors from the local 3DMM to ensure that the combined 3DMM is expressive
 while allowing accurate reconstruction. The editing controls we provide to the user are intuitive as they are extracted from
 anthropometric measurements found in the literature. Out of a large set of possible anthropometric measurements, we filter the
 ones that have meaningful generative power given the face data set. We bind the measurements to the part-based 3DMM through
 mapping matrices derived from our data set of facial scans. Our part-based 3DMM is compact yet accurate, and compared to
 other 3DMM methods, it provides a new trade-off between local and global control. We tested our approach on a data set of 135
 scans used to derive the 3DMM, plus 19 scans that served for validation. The results show that our part-based 3DMM approach
 has excellent generative properties and allows intuitive local control to the user.


Example Output:
We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute editing workflow by selecting the best sets of eigenvectors and anthropometric measurements.