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.

Ex Input:
We first consider the problem of learning k-parities in the on-line mistake-bound model: given a hidden vector x ∈ {0, 1} with |x| = k and a sequence of “questions” a1, a2, · · · ∈ {0, 1} , where the algorithm must reply to each question with 〈ai, x〉 (mod 2), what is the best tradeoff between the number of mistakes made by the algorithm and its time complexity? We improve the previous best result of Buhrman et. al. [BGM10] by an exp(k) factor in the time complexity. Second, we consider the problem of learning k-parities in the presence of classification noise of rate η ∈ (0, 1/2). A polynomial time algorithm for this problem (when η > 0 and k = ω(1)) is a longstanding challenge in learning theory. Grigorescu et al. [GRV11] showed an algorithm running in time ( n k/2 )1+4η+o(1) . Note that this algorithm inherently requires time ( n k/2 ) even when the noise rate η is polynomially small. We observe that for sufficiently small noise rate, it is possible to break the ( n k/2 ) barrier. In particular, if for some function f(n) = ω(1) and α ∈ [1/2, 1), k = n/f(n) and η = o(f(n)/ log n), then there is an algorithm for the problem with running time poly(n) · ( n k )1−α · e.

Ex Output:
On learning k-parities with and without noise


Ex Input:
In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4’, 10’, 20’ and 40’ time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization are explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.

Ex Output:
Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction


Ex Input:
Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. This has resulted is substantial duplication of effort and incompatible infrastructure across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. This TensorFlow-based infrastructure provides a complete modular deep learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications with data loading, data augmentation, network architectures, loss functions and evaluation metrics that are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted interventions.

Ex Output:

NiftyNet: a deep-learning platform for medical imaging