Are you over 18 and want to see adult content?
More Annotations
A complete backup of https://masmedicalstaffing.com
Are you over 18 and want to see adult content?
A complete backup of https://ipingpang.com
Are you over 18 and want to see adult content?
A complete backup of https://wexinc.com
Are you over 18 and want to see adult content?
A complete backup of https://kansalliskirjasto.fi
Are you over 18 and want to see adult content?
A complete backup of https://fybersearch.com
Are you over 18 and want to see adult content?
A complete backup of https://aiesec.org.br
Are you over 18 and want to see adult content?
A complete backup of https://trijin.ru
Are you over 18 and want to see adult content?
A complete backup of https://whus.org
Are you over 18 and want to see adult content?
A complete backup of https://apen4ej.org
Are you over 18 and want to see adult content?
A complete backup of https://sweetmoda7.ru
Are you over 18 and want to see adult content?
A complete backup of https://mixnetv.com.br
Are you over 18 and want to see adult content?
A complete backup of https://centurylinkquote.com
Are you over 18 and want to see adult content?
Favourite Annotations
A complete backup of dayagainsthomophobia.org
Are you over 18 and want to see adult content?
A complete backup of prontoimprese.it
Are you over 18 and want to see adult content?
A complete backup of clerawindows.com
Are you over 18 and want to see adult content?
A complete backup of hitachipowertools.com
Are you over 18 and want to see adult content?
Text
Renato Pelessoni
FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS MNIST #M, #N in Hidden Layers TfbV 20M-60M 1.02 20M-60M-150N 0.55 20M-60M-100M-150N 0.38 20M-40M-60M-80M-100M-120M-150N 0.35 Flexible, High Performance Convolutional Neural Networks for Image Classification; D. Ciresan et al. VRP WITH TIME WINDOWS MACS-VRPTW has been tested on a classical set of 56 benchmark problems (Solomon, 1987) composed of six different problem types (C1,C2,R1,R2,RC1,RC2). Each data set contains between eight to twelve 100-node problems. The names of the six problem types have the following meaning. Sets C have clustered customers whose time windowswere generated
ULRIKE KROMMER
Ulrike Krommer. November 2003, in front of Lake Lugano, with the girls. 1992: Above the University of Colorado at Boulder where Schmidhuber was a postdoc. Back to. SUPSI - DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCEPEOPLE DIRECTORYEDUCATION AND TEACHINGHOW TO REACH USACADEMIC STAFFHIGHLIGHTS The Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence. It is a joint institute of both the Faculty of Informatics of the Università della Svizzera Italiana and the Department of Innovative Technologies of MATTEO SALANI'S HOMEPAGE Matteo Salani's IDSIA homepage. Research Associate at IDSIA. Address: Galleria 2, CH-6928 Manno-Lugano, Switzerland. E-mail: Matteo.Salani@idsia.ch. Office: F207 LAURA AZZIMONTI, HOMEPAGE Laura Azzimonti Researcher in the Imprecise Probability Group at IDSIA - Istituto "Dalle Molle" di Studi sull'Intelligenza Artificiale SUPSI - Scuola Universitaria Professionale della Svizzera Italiana USI - Università della Svizzera Italiana Galleria 1, via Cantonale CH-6928 Manno (Lugano), Switzerland phone: +41 58 666 66 44 email: laura.azzimonti(at)supsi.ch WHO INVENTED BACKPROPAGATION? Who invented it? BP's modern version (also called the reverse mode of automatic differentiation) was first published in 1970 by Finnish master student Seppo Linnainmaa . In 2020, we are celebrating BP's half-century anniversary! A precursor of BP was published by Henry J. Kelley in 1960 —in 2020, we are celebrating its 60-year MIN-BDEU AND MAX-BDEU SCORES FOR LEARNING BAYESIAN NETWORKS Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks Mauro Scanagatta, Cassio P. de Campos, and Marco Za alon Istituto Dalle Molle di Studi sull’Intelligenza Arti ciale (IDSIA), Switzerland THE OCC MODEL REVISITED The OCC Model Revisited Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer Department of Information and Computing Sciences, Utrecht University, The Netherlands THE PARI-MUTUEL MODEL 6th International Symposium on Imprecise Probability: Theories and Applications, Durham, United Kingdom, 2009 The Pari-Mutuel ModelRenato Pelessoni
FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS MNIST #M, #N in Hidden Layers TfbV 20M-60M 1.02 20M-60M-150N 0.55 20M-60M-100M-150N 0.38 20M-40M-60M-80M-100M-120M-150N 0.35 Flexible, High Performance Convolutional Neural Networks for Image Classification; D. Ciresan et al. VRP WITH TIME WINDOWS MACS-VRPTW has been tested on a classical set of 56 benchmark problems (Solomon, 1987) composed of six different problem types (C1,C2,R1,R2,RC1,RC2). Each data set contains between eight to twelve 100-node problems. The names of the six problem types have the following meaning. Sets C have clustered customers whose time windowswere generated
ULRIKE KROMMER
Ulrike Krommer. November 2003, in front of Lake Lugano, with the girls. 1992: Above the University of Colorado at Boulder where Schmidhuber was a postdoc. Back to. SUPSI - DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE The Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence. It is a joint institute of both the Faculty of Informatics of the Università della Svizzera Italiana and the Department of Innovative Technologies ofNLP @ IDSIA
This web site is an entry point for NLP research at IDSIA. The NLP group at IDSIA has been established in 2019.. Together with our host Institute (), we share a joint affiliation with the University of Applied Sciences and Arts of Southern Switzerland and the Universit della Svizzera Italiana ().The dual nature of IDSIA (basic research and technology transfer) allows us to perform cutting THE OCC MODEL REVISITED The OCC Model Revisited Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer Department of Information and Computing Sciences, Utrecht University, The Netherlands HISTORY OF COMPUTER VISION CONTESTS WON BY DEEP CNNS ON GPUS Modern computer vision since 2011 relies on deep convolutional neural networks (CNNs) efficiently implemented on massively parallel graphics processing units (GPUs). Table 1 below lists important international computer vision competitions (with official submission deadlines) won by deep GPU-CNNs, ordered by date, with a focus on those contests that brought "Deep Learning Firsts" and HANDWRITING RECOGNITION WITH FAST DEEP NEURAL NETS & LSTM Winning Handwriting Recognition Competitions Through Deep Learning (2009: first really Deep Learners to win official contests). Jürgen Schmidhuber (2009-2013) . It is easier to recognize (1) isolated handwritten symbols than (2) unsegmented connected handwriting (with unknown beginnings and ends of individual letters).For both cases, our Deep Learning team achieved the best currentSWITZERLAND
Switzerland - Best Country in the World? Quality of Life. 2 of the world's top 3 most livable cities and 3 of the top 9 are located in Switzerland (Mercer survey 2012). Competitiveness. Since 2009, Switzerland has topped the overall ranking in the Global Competitiveness Report of the World Economic Forum. SEPP HOCHREITER'S FUNDAMENTAL DEEP LEARNING PROBLEM (1991) Sepp Hochreiter's Fundamental Deep Learning Problem (1991) Jürgen Schmidhuber, 2013 . Two decades later everybody is talking about Deep Learning!A first milestone of Deep Learning research was the 1991 diploma thesis of Sepp Hochreiter , my very first student, who is now a professor in Linz.His work formally showed that deep neural networks are hard to train, because they suffer from the DEEP NEURAL NETWORKS SEGMENT NEURONAL MEMBRANES IN Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan C. Cires¸an IDSIA USI-SUPSI Lugano 6900 dan@idsia.chAlessandro Giusti
ON FAST DEEP NETS FOR AGI VISION On Fast Deep Nets for AGI Vision Jurgen Schmidhuber, Dan Cires¸an, Ueli Meier, Jonathan Masci, Alex Graves¨ The Swiss AI Lab IDSIA University of Lugano & SUPSI, Switzerland FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS Flexible, High Performance Convolutional Neural Networks for Image Classification Dan C. Cires¸an, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber¨ SUPSI - DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCEPEOPLE DIRECTORYEDUCATION AND TEACHINGHOW TO REACH USACADEMIC STAFFHIGHLIGHTS The Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence. It is a joint institute of both the Faculty of Informatics of the Università della Svizzera Italiana and the Department of Innovative Technologies ofNLP @ IDSIA
This web site is an entry point for NLP research at IDSIA. The NLP group at IDSIA has been established in 2019.. Together with our host Institute (), we share a joint affiliation with the University of Applied Sciences and Arts of Southern Switzerland and the Universit della Svizzera Italiana ().The dual nature of IDSIA (basic research and technology transfer) allows us to perform cutting LAURA AZZIMONTI, HOMEPAGE Laura Azzimonti Researcher in the Imprecise Probability Group at IDSIA - Istituto "Dalle Molle" di Studi sull'Intelligenza Artificiale SUPSI - Scuola Universitaria Professionale della Svizzera Italiana USI - Università della Svizzera Italiana Galleria 1, via Cantonale CH-6928 Manno (Lugano), Switzerland phone: +41 58 666 66 44 email: laura.azzimonti(at)supsi.ch MATTEO SALANI'S HOMEPAGE Matteo Salani's IDSIA homepage. Research Associate at IDSIA. Address: Galleria 2, CH-6928 Manno-Lugano, Switzerland. E-mail: Matteo.Salani@idsia.ch. Office: F207 MIN-BDEU AND MAX-BDEU SCORES FOR LEARNING BAYESIAN NETWORKS Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks Mauro Scanagatta, Cassio P. de Campos, and Marco Za alon Istituto Dalle Molle di Studi sull’Intelligenza Arti ciale (IDSIA), Switzerland THE OCC MODEL REVISITED The OCC Model Revisited Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer Department of Information and Computing Sciences, Utrecht University, The Netherlands THE PARI-MUTUEL MODEL 6th International Symposium on Imprecise Probability: Theories and Applications, Durham, United Kingdom, 2009 The Pari-Mutuel ModelRenato Pelessoni
FIBONACCI SEQUENCE, GOLDEN SECTION, KALMAN FILTER AND Fibonacci sequence, golden section, Kalman filter and optimal control A. Benavoli1, L. Chisci2 and A. Farina3 1 Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, Manno, Switzerland, email:benavoli@gmail.com 2 DSI, Universit`a di Firenze, Firenze, Italy e-mail: chisci@dsi.unifi.it FLEXIBLE JOB SHOP PROBLEM The problem. The Flexible Job Shop Problem (FJSP) is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. The problem is to assign each operation to a machine and to order the operations on the machines, such that the maximal completion time (makespan) of all FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS Flexible, High Performance Convolutional Neural Networks for Image Classification Dan C. Cires¸an, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber¨ SUPSI - DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCEPEOPLE DIRECTORYEDUCATION AND TEACHINGHOW TO REACH USACADEMIC STAFFHIGHLIGHTS The Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence. It is a joint institute of both the Faculty of Informatics of the Università della Svizzera Italiana and the Department of Innovative Technologies ofNLP @ IDSIA
This web site is an entry point for NLP research at IDSIA. The NLP group at IDSIA has been established in 2019.. Together with our host Institute (), we share a joint affiliation with the University of Applied Sciences and Arts of Southern Switzerland and the Universit della Svizzera Italiana ().The dual nature of IDSIA (basic research and technology transfer) allows us to perform cutting LAURA AZZIMONTI, HOMEPAGE Laura Azzimonti Researcher in the Imprecise Probability Group at IDSIA - Istituto "Dalle Molle" di Studi sull'Intelligenza Artificiale SUPSI - Scuola Universitaria Professionale della Svizzera Italiana USI - Università della Svizzera Italiana Galleria 1, via Cantonale CH-6928 Manno (Lugano), Switzerland phone: +41 58 666 66 44 email: laura.azzimonti(at)supsi.ch MATTEO SALANI'S HOMEPAGE Matteo Salani's IDSIA homepage. Research Associate at IDSIA. Address: Galleria 2, CH-6928 Manno-Lugano, Switzerland. E-mail: Matteo.Salani@idsia.ch. Office: F207 MIN-BDEU AND MAX-BDEU SCORES FOR LEARNING BAYESIAN NETWORKS Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks Mauro Scanagatta, Cassio P. de Campos, and Marco Za alon Istituto Dalle Molle di Studi sull’Intelligenza Arti ciale (IDSIA), Switzerland THE OCC MODEL REVISITED The OCC Model Revisited Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer Department of Information and Computing Sciences, Utrecht University, The Netherlands THE PARI-MUTUEL MODEL 6th International Symposium on Imprecise Probability: Theories and Applications, Durham, United Kingdom, 2009 The Pari-Mutuel ModelRenato Pelessoni
FIBONACCI SEQUENCE, GOLDEN SECTION, KALMAN FILTER AND Fibonacci sequence, golden section, Kalman filter and optimal control A. Benavoli1, L. Chisci2 and A. Farina3 1 Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, Manno, Switzerland, email:benavoli@gmail.com 2 DSI, Universit`a di Firenze, Firenze, Italy e-mail: chisci@dsi.unifi.it FLEXIBLE JOB SHOP PROBLEM The problem. The Flexible Job Shop Problem (FJSP) is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. The problem is to assign each operation to a machine and to order the operations on the machines, such that the maximal completion time (makespan) of all FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS Flexible, High Performance Convolutional Neural Networks for Image Classification Dan C. Cires¸an, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber¨IDSIA ROBOTICS
Harding S. et al., Cartesian Genetic Programming for Image Processing (CGP-IP).In GPTP X, Springer (tbp). Leitner J. et al., Learning Spatial Object Localization from Vision on a Humanoid Robot.Advanced Robotic Systems, 2012. Leitner J. et al., Autonomous Learning Of Robust Visual Object Detection And Identification .ICDL/EpiRob, 2012. LAURA AZZIMONTI, HOMEPAGE Laura Azzimonti Researcher in the Imprecise Probability Group at IDSIA - Istituto "Dalle Molle" di Studi sull'Intelligenza Artificiale SUPSI - Scuola Universitaria Professionale della Svizzera Italiana USI - Università della Svizzera Italiana Galleria 1, via Cantonale CH-6928 Manno (Lugano), Switzerland phone: +41 58 666 66 44 email: laura.azzimonti(at)supsi.ch WHO INVENTED BACKPROPAGATION? Who invented it? BP's modern version (also called the reverse mode of automatic differentiation) was first published in 1970 by Finnish master student Seppo Linnainmaa . In 2020, we are celebrating BP's half-century anniversary! A precursor of BP was published by Henry J. Kelley in 1960 —in 2020, we are celebrating its 60-year DISTRIBUTED ESTIMATION IN SENSOR NETWORKS Outline Sensor Networks Communication strategies Follow on Outline 1 Sensor Networks An introduction to Sensor Networks Network architectures Distributed estimation 2 Communication strategies 3 Follow on A. Benavoli Fully Decentralized Networks SEPP HOCHREITER'S FUNDAMENTAL DEEP LEARNING PROBLEM (1991) Sepp Hochreiter's Fundamental Deep Learning Problem (1991) Jürgen Schmidhuber, 2013 . Two decades later everybody is talking about Deep Learning!A first milestone of Deep Learning research was the 1991 diploma thesis of Sepp Hochreiter , my very first student, who is now a professor in Linz.His work formally showed that deep neural networks are hard to train, because they suffer from the EXACT ALGORITHMS FOR HARD GRAPH PROBLEMS (ALGORITMI ESATTI Introduction A computational problem consists in computing, for a given input taken from a domain of instances, a solution, that is an output which satis es a given relation with the input. IMPROVED LOCAL SEARCH IN BAYESIAN NETWORKS STRUCTURE LEARNING Proceedings of Machine Learning Research vol 73:45-56, 2017 AMBN 2017 Improved Local Search in Bayesian Networks Structure Learning Mauro Scanagatta mauro@idsia.ch IDSIA , SUPSI y, USI z- Lugano, Switzerland Giorgio Corani giorgio@idsia.ch IDSIA , SUPSI y, USI z - Lugano, Switzerland Marco Za alon zaffalon@idsia.ch IDSIA - Lugano,Switzerland
FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS 2.2 Convolutional layer A convolutional layer is parametrized by the size and the number of the maps, kernel sizes, skipping factors, and the connection table. VRP WITH TIME WINDOWS MACS-VRPTW has been tested on a classical set of 56 benchmark problems (Solomon, 1987) composed of six different problem types (C1,C2,R1,R2,RC1,RC2). Each data set contains between eight to twelve 100-node problems. The names of the six problem types have the following meaning. Sets C have clustered customers whose time windowswere generated
DEEP NEURAL NETWORKS SEGMENT NEURONAL MEMBRANES IN Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan C. Cires¸an IDSIA USI-SUPSI Lugano 6900 dan@idsia.chAlessandro Giusti
SUPSI - DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCEPEOPLE DIRECTORYEDUCATION AND TEACHINGHOW TO REACH USACADEMIC STAFFHIGHLIGHTS The Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence. It is a joint institute of both the Faculty of Informatics of the Università della Svizzera Italiana and the Department of Innovative Technologies of MATTEO SALANI'S HOMEPAGE Matteo Salani's IDSIA homepage. Research Associate at IDSIA. Address: Galleria 2, CH-6928 Manno-Lugano, Switzerland. E-mail: Matteo.Salani@idsia.ch. Office: F207 LAURA AZZIMONTI, HOMEPAGE Laura Azzimonti Researcher in the Imprecise Probability Group at IDSIA - Istituto "Dalle Molle" di Studi sull'Intelligenza Artificiale SUPSI - Scuola Universitaria Professionale della Svizzera Italiana USI - Università della Svizzera Italiana Galleria 1, via Cantonale CH-6928 Manno (Lugano), Switzerland phone: +41 58 666 66 44 email: laura.azzimonti(at)supsi.ch ON THE VISUAL PERCEPTION OF FOREST TRAILS MIN-BDEU AND MAX-BDEU SCORES FOR LEARNING BAYESIAN NETWORKS Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks Mauro Scanagatta, Cassio P. de Campos, and Marco Za alon Istituto Dalle Molle di Studi sull’Intelligenza Arti ciale (IDSIA), Switzerland THE OCC MODEL REVISITED The OCC Model Revisited Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer Department of Information and Computing Sciences, Utrecht University, The Netherlands THE PARI-MUTUEL MODEL 6th International Symposium on Imprecise Probability: Theories and Applications, Durham, United Kingdom, 2009 The Pari-Mutuel ModelRenato Pelessoni
FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS MNIST #M, #N in Hidden Layers TfbV 20M-60M 1.02 20M-60M-150N 0.55 20M-60M-100M-150N 0.38 20M-40M-60M-80M-100M-120M-150N 0.35 Flexible, High Performance Convolutional Neural Networks for Image Classification; D. Ciresan et al. VRP WITH TIME WINDOWS MACS-VRPTW has been tested on a classical set of 56 benchmark problems (Solomon, 1987) composed of six different problem types (C1,C2,R1,R2,RC1,RC2). Each data set contains between eight to twelve 100-node problems. The names of the six problem types have the following meaning. Sets C have clustered customers whose time windowswere generated
ULRIKE KROMMER
Ulrike Krommer. November 2003, in front of Lake Lugano, with the girls. 1992: Above the University of Colorado at Boulder where Schmidhuber was a postdoc. Back to. SUPSI - DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCEPEOPLE DIRECTORYEDUCATION AND TEACHINGHOW TO REACH USACADEMIC STAFFHIGHLIGHTS The Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence. It is a joint institute of both the Faculty of Informatics of the Università della Svizzera Italiana and the Department of Innovative Technologies of MATTEO SALANI'S HOMEPAGE Matteo Salani's IDSIA homepage. Research Associate at IDSIA. Address: Galleria 2, CH-6928 Manno-Lugano, Switzerland. E-mail: Matteo.Salani@idsia.ch. Office: F207 LAURA AZZIMONTI, HOMEPAGE Laura Azzimonti Researcher in the Imprecise Probability Group at IDSIA - Istituto "Dalle Molle" di Studi sull'Intelligenza Artificiale SUPSI - Scuola Universitaria Professionale della Svizzera Italiana USI - Università della Svizzera Italiana Galleria 1, via Cantonale CH-6928 Manno (Lugano), Switzerland phone: +41 58 666 66 44 email: laura.azzimonti(at)supsi.ch ON THE VISUAL PERCEPTION OF FOREST TRAILS MIN-BDEU AND MAX-BDEU SCORES FOR LEARNING BAYESIAN NETWORKS Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks Mauro Scanagatta, Cassio P. de Campos, and Marco Za alon Istituto Dalle Molle di Studi sull’Intelligenza Arti ciale (IDSIA), Switzerland THE OCC MODEL REVISITED The OCC Model Revisited Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer Department of Information and Computing Sciences, Utrecht University, The Netherlands THE PARI-MUTUEL MODEL 6th International Symposium on Imprecise Probability: Theories and Applications, Durham, United Kingdom, 2009 The Pari-Mutuel ModelRenato Pelessoni
FLEXIBLE, HIGH PERFORMANCE CONVOLUTIONAL NEURAL NETWORKS MNIST #M, #N in Hidden Layers TfbV 20M-60M 1.02 20M-60M-150N 0.55 20M-60M-100M-150N 0.38 20M-40M-60M-80M-100M-120M-150N 0.35 Flexible, High Performance Convolutional Neural Networks for Image Classification; D. Ciresan et al. VRP WITH TIME WINDOWS MACS-VRPTW has been tested on a classical set of 56 benchmark problems (Solomon, 1987) composed of six different problem types (C1,C2,R1,R2,RC1,RC2). Each data set contains between eight to twelve 100-node problems. The names of the six problem types have the following meaning. Sets C have clustered customers whose time windowswere generated
ULRIKE KROMMER
Ulrike Krommer. November 2003, in front of Lake Lugano, with the girls. 1992: Above the University of Colorado at Boulder where Schmidhuber was a postdoc. Back to.NLP @ IDSIA
This web site is an entry point for NLP research at IDSIA. The NLP group at IDSIA has been established in 2019.. Together with our host Institute (), we share a joint affiliation with the University of Applied Sciences and Arts of Southern Switzerland and the Universit della Svizzera Italiana ().The dual nature of IDSIA (basic research and technology transfer) allows us to perform cuttingSWITZERLAND
Switzerland - Best Country in the World? Quality of Life. 2 of the world's top 3 most livable cities and 3 of the top 9 are located in Switzerland (Mercer survey 2012). Competitiveness. Since 2009, Switzerland has topped the overall ranking in the Global Competitiveness Report of the World Economic Forum. WHO INVENTED BACKPROPAGATION? Who invented it? BP's modern version (also called the reverse mode of automatic differentiation) was first published in 1970 by Finnish master student Seppo Linnainmaa . In 2020, we are celebrating BP's half-century anniversary! A precursor of BP was published by Henry J. Kelley in 1960 —in 2020, we are celebrating its 60-year HISTORY OF COMPUTER VISION CONTESTS WON BY DEEP CNNS ON GPUS Modern computer vision since 2011 relies on deep convolutional neural networks (CNNs) efficiently implemented on massively parallel graphics processing units (GPUs). Table 1 below lists important international computer vision competitions (with official submission deadlines) won by deep GPU-CNNs, ordered by date, with a focus on those contests that brought "Deep Learning Firsts" and THE OCC MODEL REVISITED The OCC Model Revisited Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer Department of Information and Computing Sciences, Utrecht University, The Netherlands HANDWRITING RECOGNITION WITH FAST DEEP NEURAL NETS & LSTM Winning Handwriting Recognition Competitions Through Deep Learning (2009: first really Deep Learners to win official contests). Jürgen Schmidhuber (2009-2013) . It is easier to recognize (1) isolated handwritten symbols than (2) unsegmented connected handwriting (with unknown beginnings and ends of individual letters).For both cases, our Deep Learning team achieved the best current SEPP HOCHREITER'S FUNDAMENTAL DEEP LEARNING PROBLEM (1991) Sepp Hochreiter's Fundamental Deep Learning Problem (1991) Jürgen Schmidhuber, 2013 . Two decades later everybody is talking about Deep Learning!A first milestone of Deep Learning research was the 1991 diploma thesis of Sepp Hochreiter , my very first student, who is now a professor in Linz.His work formally showed that deep neural networks are hard to train, because they suffer from the STACKED CONVOLUTIONAL AUTO-ENCODERS FOR HIERARCHICAL Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction Jonathan Masci, Ueli Meier, Dan Cire¸san, and J¨urgen Schmidhuber Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA) DEEP NEURAL NETWORKS SEGMENT NEURONAL MEMBRANES IN Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan C. Cires¸an IDSIA USI-SUPSI Lugano 6900 dan@idsia.chAlessandro Giusti
EVOLUTION OF RECURRENT NEURAL NETWORKS 3-wheeled reinforcement learning robot (with distance sensors) learns without a teacher to balance two poles with a joint indefinitely. The neurons of its recurrent neural networks (RNNs) co-evolve. Above: 3 RNNs compute quickly changing weight values for 3 fast weight networks steering the 3 wheels of the robot living in a realistic 3D physics* supsi.ch
* usi.ch
* People directory
* Reserved area
* Institute
* Research
* Education and Teaching * Highlights, News and Events* How to reach us
UNDER THE SPOTLIGHT
IDSIA: THE SWISS AI LAB The Swiss AI Lab IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence, affiliated with both the Faculty of Informatics of the Università della Svizzera Italiana and the Department of Innovative Technologies of SUPSI, the University of Applied Sciences of Southern Switzerland. We focus on machine learning (deep neural networks, reinforcement learning), operations research, data mining, and robotics. IDSIA CONTRIBUTES TO THE NEW SUPSI BACHELOR OF SCIENCE IN DATA SCIENCE AND ARTIFICIAL INTELLIGENCE. Starting from September 2020 a new three-year BSc course in Data Science and Artificial Intelligence will be offered at SUPSI. IDSIA researchers have been heavily involved in the conception and planning of this new curriculum. IDSIA RESEARCHERS WIN NINE INTERNATIONAL COMPETITIONS Our neural networks research team has won nine international competitions in machine learning and pattern recognition. Follow the link to learn more about the methods that allowed us to achieve theseresults.
NEWS
4 December 2020
PROF. ANDREA EMILIO RIZZOLI HAS BEEN APPOINTED AS DIRECTOR OF THE DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE (IDSIA USI-SUPSI)1 December 2020
IDSIA TEAM PUBLISHES ON PNAS13 November 2020
A PAPER BY CRISTINA ROTTONDI AND ALESSANDRO GIUSTI GETS A BEST PAPERAWARD
All the news
EVENTS
10 December 2020 - 10 December 2020 MONTE CARLO PLANNING AND APPLICATIONS - OLEG SZEHR29 October 2020
METABOLIC WINDOWS INTO AGEING - JANNA HASTINGS 15 October 2020 - 15 October 2020 MEDICAL IMAGE ANALYSIS FOR VIRTUAL BIOPSY AND PERSONALIZED PROGNOSTIC PROFILING IN CLINICAL PATHWAY - LARA CAVINATOQuick links
* How to reach us
* People
* Open job positionsContacts
*
Department of Innovative Technologies Dalle Molle Institute for Artificial Intelligence USI-SUPSI Galleria 2, Via Cantonale 2cCH-6928 Manno
T +41 (0)58 666 66 66 F +41 (0)58 666 66 61info@idsia.ch
*
Press Office
T +41 (0)58 666 66 07 dti.comunicazione@supsi.ch* © 2020 SUPSI
*
* Work with us
* Contacts
* Sitemap
* Site index
* Disclaimer
INSTITUTIONAL SOCIAL NETWORKS*
*
*
*
*
st.wwwsupsi@supsi.chDetails
Copyright © 2024 ArchiveBay.com. All rights reserved. Terms of Use | Privacy Policy | DMCA | 2021 | Feedback | Advertising | RSS 2.0