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Subjects / keywords: Computer science; Technological innovations; Machine learning; Artificial intelligence

Subjects / keywords: Neural networks (Computer science); Artificial intelligence; Information networks; Machine learning

Subjects / keywords: Deep learning; Artificial intelligence; Computer science; Segmentation; Field (mathematics); Convolutional neural network; State of art; Image segmentation; Open research; Machine learning; Image (mathematics); Data scie...

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance ...

Subjects / keywords: Discriminative model; Computer science; Artificial intelligence; Domain (mathematical analysis); Backpropagation; Artificial neural network; Context (archaeology); Test data; Transfer of learning; Identification (biology...

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has recei...

Subjects / keywords: Champion; Artificial intelligence; Artificial neural network; Reinforcement learning; Computer science; Selection (genetic algorithm); Tree (set theory); Machine learning; Domain (mathematical analysis); Mathematics; Pol...

Subjects / keywords: Convolutional neural network; Computer science; Deep learning; Artificial intelligence; Activation function; Artificial neural network; Computation; Machine learning; Graphics; Pattern recognition (psychology); Algorithm...

We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body part...

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with...