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: Combining domain knowledge models with neural models has been challenging. End-to-end trained neural models often perform better (lower Mean Square Error) than domain knowledge models or domain/neural combinations, and the combination is inefficient to train. In this paper, we demonstrate that by composing domain models with machine learning models, by using extrapolative testing sets, and invoking decorrelation objective functions, we create models which can predict more complex systems. The models are interpretable, extrapolative, data-efficient, and capture predictable but complex non-stochastic behavior such as unmodeled degrees of freedom and systemic measurement noise. We apply this improved modeling paradigm to several simulated systems and an actual physical system in the context of system identification. Several ways of composing domain models with neural models are examined for time series, boosting, bagging, and auto-encoding on various systems of varying complexity and non-linearity. Although this work is preliminary, we show that the ability to combine models is a very promising direction for neural modeling.
Example Output: Improved modeling of complex systems uses hybrid neural/domain model composition, new decorrelation loss functions and extrapolative test sets

Example Input: Deep neural networks are known to be vulnerable to adversarial perturbations. In this paper, we bridge adversarial robustness of neural nets with Lyapunov stability of dynamical systems. From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets. This decoupled training method allows us to add constraints to the optimization, which makes the deep model more robust. The constrained optimization problem can be formulated as a semi-definite programming problem and hence can be solved efficiently. Experiments show that our method effectively improves deep model's adversarial robustness.
Example Output: An adversarial defense method bridging robustness of deep neural nets with Lyapunov stability

Example Input: Compressed forms of deep neural networks are essential in deploying large-scale
computational models on resource-constrained devices. Contrary to analogous
domains where large-scale systems are build as a hierarchical repetition of small-
scale units, the current practice in Machine Learning largely relies on models with
non-repetitive components. In the spirit of molecular composition with repeating
atoms, we advance the state-of-the-art in model compression by proposing Atomic
Compression Networks (ACNs), a novel architecture that is constructed by recursive
repetition of a small set of neurons. In other words, the same neurons with the
same weights are stochastically re-positioned in subsequent layers of the network.
Empirical evidence suggests that ACNs achieve compression rates of up to three
orders of magnitudes compared to fine-tuned fully-connected neural networks (88
to 1116 reduction) with only a fractional deterioration of classification accuracy
(0.15% to 5.33%). Moreover our method can yield sub-linear model complexities
and permits learning deep ACNs with less parameters than a logistic regression
with no decline in classification accuracy.
Example Output:
We advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons.