TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation. Keywordsmachine learning; deep learning; TensorFlow; Deep Learning4j; H2O; MNIST; multicore CPU; GPU.

SOLUTION: Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single- Threaded and Multi-Threaded Modes

PROBLEM: Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

SOLUTION: Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples

PROBLEM: We formulate a phonetic-prosodic space based on attributes as perceptual observables, rather than articulatory specifications. We propose an alphabet as markers in the phonetic subspace, aiming for a resolution sufficient to support recognition of all spoken languages. The prosodic subspace is made up of directly measurable physical variables. With the proposed alphabet, traditional diphthongs naturally generalize to a broader class of language-neutral phonotactic constraints, indicating a correlation structure similar to that of the traditional sonority-based syllable. We define a stochastic structure on the phone strings based on this diphthongal constraint, and show how a specific spoken language can be defined as a specific set of probability distributions of this stochastic structure. Furthermore, phonological variations within a spoken language can be modeled as varying probability distributions restricted to the phonetic subspace, conditioned on different values in the prosodic subspace.

SOLUTION:
A 10-dimensional Phonetic-prosodic Space and its Stochastic Structure A framework for probabilistic modeling of spoken languages and their phonology