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.

[Q]: Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, code completion, and fault localization. However, most existing program embeddings are based on syntactic features of programs, such as token sequences or abstract syntax trees. Unlike images and text, a program has well-deﬁned semantics that can be difﬁcult to capture by only considering its syntax (i.e. syntactically similar programs can exhibit vastly different run-time behavior), which makes syntax-based program embeddings fundamentally limited. We propose a novel semantic program embedding that is learned from program execution traces. Our key insight is that program states expressed as sequential tuples of live variable values not only capture program semantics more precisely, but also offer a more natural ﬁt for Recurrent Neural Networks to model. We evaluate different syntactic and semantic program embeddings on the task of classifying the types of errors that students make in their submissions to an introductory programming class and on the CodeHunt education platform. Our evaluation results show that the semantic program embeddings signiﬁcantly outperform the syntactic program embeddings based on token sequences and abstract syntax trees. In addition, we augment a search-based program repair system with predictions made from our semantic embedding and demonstrate signiﬁcantly improved search efﬁciency.

[A]: A new way of learning semantic program embedding


[Q]: Deterministic models are approximations of reality that are often easier to build and interpret than stochastic alternatives.  
 Unfortunately, as nature is capricious, observational data can never be fully explained by deterministic models in practice.  
 Observation and process noise need to be added to adapt deterministic models to behave stochastically, such that they are capable of explaining and extrapolating from noisy data.
 Adding process noise to deterministic simulators can induce a failure in the simulator resulting in no return value for certain inputs -- a property we describe as ``brittle.''
 We investigate and address the wasted computation that arises from these failures, and the effect of such failures on downstream inference tasks.
 We show that performing inference in this space can be viewed as rejection sampling, and train a conditional normalizing flow as a proposal over noise values such that there is a low probability that the simulator crashes, increasing computational efficiency and inference fidelity for a fixed sample budget when used as the proposal in an approximate inference algorithm.
[A]: We learn a conditional autoregressive flow to propose perturbations that don't induce simulator failure, improving inference performance.


[Q]: We present the first verification that a neural network for perception tasks produces
 a correct output within a specified tolerance for every input of interest. We define
 correctness relative to a specification which identifies 1) a state space consisting of
 all relevant states of the world and 2) an observation process that produces neural
 network inputs from the states of the world. Tiling the state and input spaces with
 a finite number of tiles, obtaining ground truth bounds from the state tiles and
 network output bounds from the input tiles, then comparing the ground truth and
 network output bounds delivers an upper bound on the network output error for
 any input of interest. Results from two case studies highlight the ability of our
 technique to deliver tight error bounds for all inputs of interest and show how the
 error bounds vary over the state and input spaces.
[A]:
We present the first verification that a neural network for perception tasks produces a correct output within a specified tolerance for every input of interest.