Listing 1 - 10 of 21 | << page >> |
Sort by
|
Choose an application
The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.
Information technology industries --- information theory --- variational inference --- machine learning --- learnability --- information bottleneck --- representation learning --- conspicuous subset --- stochastic neural networks --- mutual information --- neural networks --- information --- bottleneck --- compression --- classification --- optimization --- classifier --- decision tree --- ensemble --- deep neural networks --- regularization methods --- information bottleneck principle --- deep networks --- semi-supervised classification --- latent space representation --- hand crafted priors --- learnable priors --- regularization --- deep learning --- information theory --- variational inference --- machine learning --- learnability --- information bottleneck --- representation learning --- conspicuous subset --- stochastic neural networks --- mutual information --- neural networks --- information --- bottleneck --- compression --- classification --- optimization --- classifier --- decision tree --- ensemble --- deep neural networks --- regularization methods --- information bottleneck principle --- deep networks --- semi-supervised classification --- latent space representation --- hand crafted priors --- learnable priors --- regularization --- deep learning
Choose an application
INVENTORY MODELS[OPTIMAL-] --- PRODUCTION[MULTISTAGE-/BOTTLENECK PROBLEMS] --- DECISION PROCESSES[MARKOVIAN-] --- VARIATION METHOD IN DECISION PROCESSES --- DECISION PROCESSES[CONTINUOUS STOCHASTIC-] --- ALLOCATION PROBLEMS[MULTISTAGE-] --- GAMES[MULTISTAGE-] --- DECISION PROCESSES[STOCHASTIC MULTISTAGE-] --- PROGRAMMING[DYNAMIC-] --- BOTTLENECK PROBLEMS IN MULTISTAGE PRODUCTION --- Programming --- Dynamic programming
Choose an application
The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.
Information technology industries --- information theory --- variational inference --- machine learning --- learnability --- information bottleneck --- representation learning --- conspicuous subset --- stochastic neural networks --- mutual information --- neural networks --- information --- bottleneck --- compression --- classification --- optimization --- classifier --- decision tree --- ensemble --- deep neural networks --- regularization methods --- information bottleneck principle --- deep networks --- semi-supervised classification --- latent space representation --- hand crafted priors --- learnable priors --- regularization --- deep learning
Choose an application
The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.
information theory --- variational inference --- machine learning --- learnability --- information bottleneck --- representation learning --- conspicuous subset --- stochastic neural networks --- mutual information --- neural networks --- information --- bottleneck --- compression --- classification --- optimization --- classifier --- decision tree --- ensemble --- deep neural networks --- regularization methods --- information bottleneck principle --- deep networks --- semi-supervised classification --- latent space representation --- hand crafted priors --- learnable priors --- regularization --- deep learning
Choose an application
The building of a new manufacturing and logistical facility is always a source of challenge for a company. Every aspect of the project must be carefully addressed. This is the case of the company IBA, which will open a new plant to answer the growing trend of its main business line, the proton therapy. Therefore, a project team was build. An intern was also hired to support the project and to more specifically cover the future manoeuvring area. The importance of the manoeuvring area does not have to be underestimated. It is the entrance of the building and does not have to be a bottleneck. The aim of this project-dissertation is to assess the manoeuvring area and the potential risks (safety, efficiency, feasibility, fluidness) by first recommending an optimal organisation of the manoeuvring area (location of the docks, flows) and afterwards by quantitatively analysing the zone. A simulation of the manoeuvring area has been developed in the software Anylogic to undertake the analysis. First of all, literature about simulation modelling is summarized in order to provide the reader with first knowledge of this powerful problem-solving. In addition, the methodology used to specifically implement the simulation in the software Anylogic is presented. Afterwards, the final recommendation for an organisation of the zone is described. This organisation is the result of workshops supported by different versions of the simulation. Then, the organisation chosen, the simulation model has been further developed in order to quantitatively assess the manoeuvring area according to different scenarios combining two factors, the volume and the number of workers. Different utilization rates and performances related to the queues were measured and analysed. It allowed identifying whether the docks or the number of workers can be characterized as a bottleneck. Finally, recommendations to tackle the risks that might occur in the future are given. To conclude, the perspectives of the simulation tool on one hand for a manager and on the other hand for IBA’s future plant are broached.
Choose an application
The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
History of engineering & technology --- autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention --- autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention
Choose an application
The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
History of engineering & technology --- autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention
Choose an application
The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention
Choose an application
The interaction of ionising radiation with atomic and/or molecular ions is a fundamental process in nature, with implications for the understanding of many laboratory and astrophysical plasmas. At short wavelengths, the photon–ion interactions lead to inner-shell and multiple electron excitations, leading to demands on appropriate laboratory developments of sources and detectors and requiring advanced theoretical treatments which take into account many-body electron-correlation effects. This book includes a range of papers based on different short wavelength photon sources including recent facility and instrumental developments. Topics include experimental photoabsorption studies with laser-produced plasmas and photoionization of atomic and molecular ions with synchrotron and FEL sources, including modifications of a cylindrical mirror analyzer for high efficiency photoelectron spectroscopy on ion beams. Theoretical investigations include the effects of FEL fluctuations on autoionization line shapes, multiple sequential ionization by intense fs XUV pulses, photoelectron angular distributions for non-resonant two-photon ionization, inner-shell photodetachment of Na- and spin-polarized fluxes from fullerene anions.
2s2p --- Lithium-ion --- auto-ionization --- free electron laser --- stochastic average --- time dependent density matrix --- photoionization --- multiple ionization --- many-electron processes --- absolute cross sections --- synchrotron radiation --- collisional-radiative model --- laser-produced plasma, ion distribution --- ionization bottleneck --- radiative recombination --- collisional ioniztion --- three-body recombination --- nonlinear photoionization --- nonlinear interaction --- Cooper minimum --- angular distributions --- atomic ions --- dual-laser plasma technique --- photodetachment --- inner-shell phenomena --- electron spectroscopy --- ion beam --- spin-polarization --- fullerene anions --- endohedral fullerene anions --- NH+ --- molecular ion --- K-shell --- merged-beam --- Pb-Sn alloys --- EUV emission of high Z materials --- collisional radiative model --- Cowan suite of Codes --- ions --- free-electron laser --- krypton --- femtosecond pulses --- photoelectron spectroscopy --- atomic data --- inner-shell photoionization --- atomic nitrogen ion --- n/a
Choose an application
The interaction of ionising radiation with atomic and/or molecular ions is a fundamental process in nature, with implications for the understanding of many laboratory and astrophysical plasmas. At short wavelengths, the photon–ion interactions lead to inner-shell and multiple electron excitations, leading to demands on appropriate laboratory developments of sources and detectors and requiring advanced theoretical treatments which take into account many-body electron-correlation effects. This book includes a range of papers based on different short wavelength photon sources including recent facility and instrumental developments. Topics include experimental photoabsorption studies with laser-produced plasmas and photoionization of atomic and molecular ions with synchrotron and FEL sources, including modifications of a cylindrical mirror analyzer for high efficiency photoelectron spectroscopy on ion beams. Theoretical investigations include the effects of FEL fluctuations on autoionization line shapes, multiple sequential ionization by intense fs XUV pulses, photoelectron angular distributions for non-resonant two-photon ionization, inner-shell photodetachment of Na- and spin-polarized fluxes from fullerene anions.
Research & information: general --- 2s2p --- Lithium-ion --- auto-ionization --- free electron laser --- stochastic average --- time dependent density matrix --- photoionization --- multiple ionization --- many-electron processes --- absolute cross sections --- synchrotron radiation --- collisional-radiative model --- laser-produced plasma, ion distribution --- ionization bottleneck --- radiative recombination --- collisional ioniztion --- three-body recombination --- nonlinear photoionization --- nonlinear interaction --- Cooper minimum --- angular distributions --- atomic ions --- dual-laser plasma technique --- photodetachment --- inner-shell phenomena --- electron spectroscopy --- ion beam --- spin-polarization --- fullerene anions --- endohedral fullerene anions --- NH+ --- molecular ion --- K-shell --- merged-beam --- Pb-Sn alloys --- EUV emission of high Z materials --- collisional radiative model --- Cowan suite of Codes --- ions --- free-electron laser --- krypton --- femtosecond pulses --- photoelectron spectroscopy --- atomic data --- inner-shell photoionization --- atomic nitrogen ion --- 2s2p --- Lithium-ion --- auto-ionization --- free electron laser --- stochastic average --- time dependent density matrix --- photoionization --- multiple ionization --- many-electron processes --- absolute cross sections --- synchrotron radiation --- collisional-radiative model --- laser-produced plasma, ion distribution --- ionization bottleneck --- radiative recombination --- collisional ioniztion --- three-body recombination --- nonlinear photoionization --- nonlinear interaction --- Cooper minimum --- angular distributions --- atomic ions --- dual-laser plasma technique --- photodetachment --- inner-shell phenomena --- electron spectroscopy --- ion beam --- spin-polarization --- fullerene anions --- endohedral fullerene anions --- NH+ --- molecular ion --- K-shell --- merged-beam --- Pb-Sn alloys --- EUV emission of high Z materials --- collisional radiative model --- Cowan suite of Codes --- ions --- free-electron laser --- krypton --- femtosecond pulses --- photoelectron spectroscopy --- atomic data --- inner-shell photoionization --- atomic nitrogen ion
Listing 1 - 10 of 21 | << page >> |
Sort by
|