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Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One… Expand Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014… Expand We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive… Expand Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier… Expand We propose two novel model architectures for computing continuous vector
representations of words from very large data sets. The… Expand A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is… Expand This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This… Expand The previously developed particle mesh Ewald method is reformulated in terms of efficient B‐spline interpolation of the structure… Expand The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient… Expand This paper presents new algorithms for the maximum flow problem, the Hitchcock transportation problem, and the general minimum… Expand