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Materie / parole chiave: Artificial intelligence; Law and legislation; Quantum computing

Abstract Together with ‘On Computable Numbers’, ‘Computing Machinery and Intelligence’ forms Turing’s best-known work. This elegant and sometimes amusing essay was originally published in 1950 in the leading ph...

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We...

Materie / parole chiave: Representativeness heuristic; Heuristics; Class (philosophy); Computer science; Process (computing); Object (grammar); Event (particle physics); Value (mathematics); Artificial intelligence; Psychology; Machine learning;...

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques am...

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two...

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library...

The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framew...

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...

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histogra...

Understanding the evolutionary history of living organisms is a central problem in biology. Until recently the ability to infer evolutionary relationships was limited by the amount of DNA sequence data available, but new...

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVR...

During the last decade, the data sizes have grown faster than the speed of processors. In this context, the capabilities of statistical machine learning methods is limited by the computing time rather than the sample siz...