Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Formal verification of machine learning models has attracted attention recently, and significant progress has been made on proving simple properties like robustness to small perturbations of the input features.
In this context, it has also been observed that folding the verification procedure into training makes it easier to train verifiably robust models.
In this paper, we extend the applicability of verified training by extending it to (1) recurrent neural network architectures and (2) complex specifications that go beyond simple adversarial robustness, particularly specifications that capture temporal properties like requiring that a robot periodically visits a charging station or that a language model always produces sentences of bounded length.
Experiments show that while models trained using standard training often violate desired specifications, our verified training method produces models that both perform well (in terms of test error or reward) and can be shown to be provably consistent with specifications.
While deep neural networks (DNNs) have shown immense progress on diverse tasks (Sutskever et al., 2014; Mnih et al., 2015; Silver et al., 2016) , they are often deployed without formal guarantees of their correctness and functionality.
Their performance is typically evaluated using test data, or sometimes with adversarial evaluation (Carlini & Wagner, 2017; Ebrahimi et al., 2018; Wang et al., 2019) .
However, such evaluation does not provide formal guarantees regarding the absence of rare but possibly catastrophic failures (Administration; Board; Ross & Swetlitz, 2018) .
Researchers have therefore started investigating formal verification techniques for DNNs.
Most of the focus in this direction has been restricted to feedforward networks and robustness to adversarial perturbations (Tjeng et al., 2017; Raghunathan et al., 2018b; Ko et al., 2019) .
However, many practically relevant systems involve DNNs that lead to sequential outputs (e.g., an RNN that generates captions for images, or the states of an RL agent).
These sequential outputs can be interpreted as real-valued, discrete-time signals.
For such signals, it is of interest to provide guarantees with respect to temporal specifications (e.g., absence of repetitions in a generated sequence, or that a generated sequence halts appropriately).
Temporal logic provides a compact and intuitive formalism for capturing such properties that deal with temporal abstractions.
Here, we focus on Signal Temporal Logic (STL) (Donzé & Maler, 2010) as the specification language and exploit its quantitative semantics to integrate a verification procedure into training to provide guarantees with regard to temporal specifications.
Our approach builds on recent work , which is based on propagating differentiable numerical bounds through DNNs, to include specifications that go beyond adversarial robustness.
Additionally, we propose extensions to ; that allow us to train auto-regressive GRUs/RNNs to certifiably satisfy temporal specifications.
We focus on the problem of verified training for consistency rather than post-facto verification.
To summarize, our contributions are as:
• We present extensions to ; that allow us to extend verified training to novel architectures and specifications, including complex temporal specifications.
To handle the auto-regressive decoder often used in RNN-based systems, we leverage differentiable approximations of the non-differentiable operations.
• We empirically demonstrate the applicability of our approach to ensure verifiable consistency with temporal specifications while maintaining the ability of neural networks to achieve high accuracy on the underlying tasks across domains.
For supervised learning, verified training on the train-data enables us to provide similar verification guarantees for unseen test-data.
• We show that verified training results in robust DNNs whose specification conformance is significantly easier to guarantee than those trained adversarially or with data augmentation.
Temporal properties are commonly desired from DNNs in settings where the outputs have a sequential nature.
We extend verified training to tasks that require temporal properties to be satisfied, and to architectures such as auto-regressive RNNs whose outputs have a sequential nature.
Our experiments suggest that verified training leads to DNNs that are more verifiable, and often with fewer failures.
Future work includes extending verification/verified training to unbounded temporal properties.
Another important direction is to develop better bound propagation techniques that can be leveraged for verified training.
In the RL setting, an important direction is data-driven verification in the absence of a known model of the environment.
Neural Network Verification for Temporal Properties and Sequence Generation Models