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: Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.

A: We pose that generative models' likelihoods are excessively influenced by the input's complexity, and propose a way to compensate it when detecting out-of-distribution inputs
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Q: Performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Many image classification tasks, such as those related to cellular imaging, exhibit invariance to rotation. In particular, to aid convolutional neural networks in learning rotation invariance, we consider a simple, efficient conic convolutional scheme that encodes rotational equivariance, along with a method for integrating the magnitude response of the 2D-discrete-Fourier transform (2D-DFT) to encode global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). We evaluated the efficacy of CFNet as compared to a standard CNN and group-equivariant CNN (G-CNN) for several different image classification tasks and demonstrated improved performance, including classification accuracy, computational efficiency, and its robustness to hyperparameter selection. Taken together, we believe CFNet represents a new scheme that has the potential to improve many imaging analysis applications.

A: We propose conic convolution and the 2D-DFT to encode rotation equivariance into an neural network.
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Q: We introduce a model that learns to convert simple hand drawings
into graphics programs written in a subset of \LaTeX.~ The model
combines techniques from deep learning and program synthesis. We
learn a convolutional neural network that proposes plausible drawing
primitives that explain an image. These drawing primitives are like
a trace of the set of primitive commands issued by a graphics
program. We learn a model that uses program synthesis techniques to
recover a graphics program from that trace. These programs have
constructs like variable bindings, iterative loops, or simple kinds
of conditionals. With a graphics program in hand, we can correct
errors made by the deep network and extrapolate drawings. Taken
together these results are a step towards agents that induce useful,
human-readable programs from perceptual input.

A:
Learn to convert a hand drawn sketch into a high-level program
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