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.

Input: Consider Input: Sliding window convolutional networks (ConvNets) have become a popular approach to computer vision problems such as image segmentation, and object detection and localization. Here we consider the problem of inference, the application of a previously trained ConvNet, with emphasis on 3D images. Our goal is to maximize throughput, defined as average number of output voxels computed per unit time. Other things being equal, processing a larger image tends to increase throughput, because fractionally less computation is wasted on the borders of the image. It follows that an apparently slower algorithm may end up having higher throughput if it can process a larger image within the constraint of the available RAM. We introduce novel CPU and GPU primitives for convolutional and pooling layers, which are designed to minimize memory overhead. The primitives include convolution based on highly efficient pruned FFTs. Our theoretical analyses and empirical tests reveal a number of interesting findings. For some ConvNet architectures, cuDNN is outperformed by our FFT-based GPU primitives, and these in turn can be outperformed by our CPU primitives. The CPU manages to achieve higher throughput because of its fast access to more RAM. A novel primitive in which the GPU accesses host RAM can significantly increase GPU throughput. Finally, a CPU-GPU algorithm achieves the greatest throughput of all, 10 or more than other publicly available implementations of sliding window 3D ConvNets. All of our code has been made available as open source project.

Output: ZNNi Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs

Input: Consider Input: The paper addresses the problem of learning a regression model parameterized by a fixedrank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixed-rank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks.

Output: Regression on fixed-rank positive semidefinite matrices: a Riemannian approach

Input: Consider Input: <lb>We endow prioritised default logic (PDL) with argumentation semantics<lb>using the ASPIC framework for structured argumentation, and prove<lb>that the conclusions of the justified arguments are exactly the prioritised<lb>default extensions. Argumentation semantics for PDL will allow for the<lb>application of argument game proof theories to the process of inference<lb>in PDL, making the reasons for accepting a conclusion transparent and<lb>the inference process more intuitive. This also opens up the possibility for<lb>argumentation-based distributed reasoning and communication amongst<lb>agents with PDL representations of mental attitudes.
Output: Argumentation Semantics for Prioritised Default Logic