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 Q]: Adoption of deep learning in safety-critical systems raise the need for understanding what deep neural networks do not understand. Several methodologies to estimate model uncertainty have been proposed, but these methodologies constrain either how the neural network is trained or constructed. We present Outlier Detection In Neural networks (ODIN), an assumption-free method for detecting outlier observations during prediction, based on principles widely used in manufacturing process monitoring. By using a linear approximation of the hidden layer manifold, we add prediction-time outlier detection to models after training without altering architecture or training. We demonstrate that ODIN efficiently detect outliers during prediction on Fashion-MNIST, ImageNet-synsets and speech command recognition.
[EX A]: An add-on method for deep learning to detect outliers during prediction-time

[EX Q]: Plagiarism and text reuse become more available with the Internet development. Therefore it is important to check scientific papers for the fact of cheating, especially in Academia. Existing systems of plagiarism detection show the good performance and have a huge source databases. Thus now it is not enough just to copy the text as is from the source document to get the original work. Therefore, another type of plagiarism become popular -- cross-lingual plagiarism. We present a CrossLang system for such kind of plagiarism detection for English-Russian language pair.
[EX A]: A system for cross-lingual (English-Russian) plagiarism detection

[EX Q]: We study the problem of generating source code in a strongly typed,
 Java-like programming language, given a label (for example a set of
 API calls or types) carrying a small amount of information about the
 code that is desired. The generated programs are expected to respect a
 `"realistic" relationship between programs and labels, as exemplified
 by a corpus of labeled programs available during training.

 Two challenges in such *conditional program generation* are that
 the generated programs must satisfy a rich set of syntactic and
 semantic constraints, and that source code contains many low-level
 features that impede learning.  We address these problems by training
 a neural generator not on code but on *program sketches*, or
 models of program syntax that abstract out names and operations that
 do not generalize across programs. During generation, we infer a
 posterior distribution over sketches, then concretize samples from
 this distribution into type-safe programs using combinatorial
 techniques.  We implement our ideas in a system for generating
 API-heavy Java code, and show that it can often predict the entire
 body of a method given just a few API calls or data types that appear
 in the method.
[EX A]:
We give a method for generating type-safe programs in a Java-like language, given a small amount of syntactic information about the desired code.