instruction:
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.
question:
Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective.

 In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.
answer:
We introduce Spherical CNNs, a convolutional network for spherical signals, and apply it to 3D model recognition and molecular energy regression.


question:
Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-the-art result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. Code is immediately available.
answer:
Mix plain SGD and momentum (or do something similar with Adam) for great profit.


question:
Given the fast development of analysis techniques for NLP and speech
 processing systems, few systematic studies have been conducted to
 compare the strengths and weaknesses of each method.  As a step in
 this direction we study the case of representations of phonology in
 neural network models of spoken language. We use two commonly applied
 analytical techniques, diagnostic classifiers and representational
 similarity analysis, to quantify to what extent neural activation
 patterns encode phonemes and phoneme sequences. We manipulate two
 factors that can affect the outcome of analysis. First, we investigate
 the role of learning by comparing neural activations extracted from
 trained versus randomly-initialized models. Second, we examine the
 temporal scope of the activations by probing both local activations
 corresponding to a few milliseconds of the speech signal, and global
 activations pooled over the whole utterance. We conclude that
 reporting analysis results with randomly initialized models is
 crucial, and that global-scope methods tend to yield more consistent
 and interpretable results and we recommend their use as a complement
 to local-scope diagnostic methods.
answer:

We study representations of phonology in neural network models of spoken language with several variants of analytical techniques.