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Probabilistic Programming for Inverse Problems in Physical Sciences
Probabilistic Programming for Inverse Problems in Physical Sciences

13/Apr/2021
13/Apr/2021
 at 
12:00
12:00

Speaker:

Atilim Güneş Baydin
Atilim Güneş Baydin

Institution:

University of Oxford
University of Oxford

Language :

EN
EN

Type :

Webinar
Webinar

Description:

Machine learning enables new approaches to inverse problems in many fields of science. We present a novel probabilistic programming framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via amortized inference where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.

Dr Atilim Güneş Baydin is a Departmental Lecturer in machine learning at the Department of Computer Science and a Senior Researcher in machine learning at the Department of Engineering Science, University of Oxford. He works with Philip H. S. Torr as a member of Torr Vision Group. He is also a Research Member of the Common Room at Kellogg College, a research consultant for Microsoft Research Cambridge, and a member of European Lab for Learning and Intelligent Systems (ELLIS).

Machine learning enables new approaches to inverse problems in many fields of science. We present a novel probabilistic programming framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via amortized inference where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.

Dr Atilim Güneş Baydin is a Departmental Lecturer in machine learning at the Department of Computer Science and a Senior Researcher in machine learning at the Department of Engineering Science, University of Oxford. He works with Philip H. S. Torr as a member of Torr Vision Group. He is also a Research Member of the Common Room at Kellogg College, a research consultant for Microsoft Research Cambridge, and a member of European Lab for Learning and Intelligent Systems (ELLIS).

If you are interested in participating in this webinar, please send an e-mail stating your interest to jsabater@iiia.csic.es.

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13/Apr/2021
13/Apr/2021
 at 
12:00
12:00

Probabilistic Programming for Inverse Problems in Physical Sciences
Probabilistic Programming for Inverse Problems in Physical Sciences

Atilim Güneş Baydin
Atilim Güneş Baydin
 - 
University of Oxford
University of Oxford
 - 
EN
EN

Machine learning enables new approaches to inverse problems in many fields of science. We present a novel probabilistic programming framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via amortized inference where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.

Dr Atilim Güneş Baydin is a Departmental Lecturer in machine learning at the Department of Computer Science and a Senior Researcher in machine learning at the Department of Engineering Science, University of Oxford. He works with Philip H. S. Torr as a member of Torr Vision Group. He is also a Research Member of the Common Room at Kellogg College, a research consultant for Microsoft Research Cambridge, and a member of European Lab for Learning and Intelligent Systems (ELLIS).

Machine learning enables new approaches to inverse problems in many fields of science. We present a novel probabilistic programming framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via amortized inference where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.

Dr Atilim Güneş Baydin is a Departmental Lecturer in machine learning at the Department of Computer Science and a Senior Researcher in machine learning at the Department of Engineering Science, University of Oxford. He works with Philip H. S. Torr as a member of Torr Vision Group. He is also a Research Member of the Common Room at Kellogg College, a research consultant for Microsoft Research Cambridge, and a member of European Lab for Learning and Intelligent Systems (ELLIS).

 
20/Apr/2021
20/Apr/2021
 at 
11:30
11:30

Fuzzy Horn clauses in artificial intelligence: A study of free models, and applications in art painting style categorization
Fuzzy Horn clauses in artificial intelligence: A study of free models, and applications in art painting style categorization

Vicent Costa
Vicent Costa
 - 
IIIA-CSIC - UAB
IIIA-CSIC - UAB
 - 
EN
EN

This webinar is a PhD thesis defence.

This PhD thesis contributes to the systematic study of Horn clauses of predicate fuzzy logics and their use in knowledge representation for the design of an art painting style classification algorithm. We first focus the study on relevant notions in logic programming, such as free models and Herbrand structures in mathematical fuzzy logic. We show the existence of free models in fuzzy universal Horn classes, and we prove that every equality-free consistent universal Horn fuzzy theory has a Herbrand model. Two notions of minimality of free models are introduced, and we show that these notions are equivalent in the case of fully named structures. Then, we use Horn clauses combined with qualitative modelling as a fuzzy knowledge representation framework for art painting style categorization. Finally, we design a style painting classifier based on evaluated Horn clauses, qualitative colour descriptors, and explanations. This algorithm, called l-SHE, provides reasons for the obtained results and obtains percentages of accuracy in the experimentation that are competitive.

This webinar is a PhD thesis defence.

This PhD thesis contributes to the systematic study of Horn clauses of predicate fuzzy logics and their use in knowledge representation for the design of an art painting style classification algorithm. We first focus the study on relevant notions in logic programming, such as free models and Herbrand structures in mathematical fuzzy logic. We show the existence of free models in fuzzy universal Horn classes, and we prove that every equality-free consistent universal Horn fuzzy theory has a Herbrand model. Two notions of minimality of free models are introduced, and we show that these notions are equivalent in the case of fully named structures. Then, we use Horn clauses combined with qualitative modelling as a fuzzy knowledge representation framework for art painting style categorization. Finally, we design a style painting classifier based on evaluated Horn clauses, qualitative colour descriptors, and explanations. This algorithm, called l-SHE, provides reasons for the obtained results and obtains percentages of accuracy in the experimentation that are competitive.

 
27/Apr/2021
27/Apr/2021
 at 
12:00
12:00

UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence
UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence

Natalia Criado Pacheco
Natalia Criado Pacheco
 - 
UKRI Centre - King's College London
UKRI Centre - King's College London
 - 
EN
EN

The overarching aim of the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted Artificial Intelligence (STAI) is to train the first generation of AI scientists and engineers in methods of safe and trusted AI. An AI system is considered safe when we can provide assurances about the correctness of its behaviour, and it is considered trusted if the average user can have confidence in the system and its decision making. The CDT focuses particularly on the use of model-based AI techniques for ensuring the safety and trustworthiness of AI systems. Model-based AI techniques provide an explicit language for representing, analysing and reasoning about systems and their behaviours. Models can be verified and solutions based on them can be guaranteed as safe and correct, and models can provide human-understandable explanations and support user collaboration and interaction with AI – key for developing trust in a system. In this talk, we will present the central vision, programme, and core research areas.

Dr Natalia Criado is a Senior Lecturer in Computer Science at King's College London and a member of the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted Artificial Intelligence (STAI).

The overarching aim of the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted Artificial Intelligence (STAI) is to train the first generation of AI scientists and engineers in methods of safe and trusted AI. An AI system is considered safe when we can provide assurances about the correctness of its behaviour, and it is considered trusted if the average user can have confidence in the system and its decision making. The CDT focuses particularly on the use of model-based AI techniques for ensuring the safety and trustworthiness of AI systems. Model-based AI techniques provide an explicit language for representing, analysing and reasoning about systems and their behaviours. Models can be verified and solutions based on them can be guaranteed as safe and correct, and models can provide human-understandable explanations and support user collaboration and interaction with AI – key for developing trust in a system. In this talk, we will present the central vision, programme, and core research areas.

Dr Natalia Criado is a Senior Lecturer in Computer Science at King's College London and a member of the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted Artificial Intelligence (STAI).

 
06/Apr/2021
06/Apr/2021

How to get the best of your scientific communication skills
How to get the best of your scientific communication skills

Carme Roig
Carme Roig
 - 
Departament d'Educació - Generalitat de Catalunya
Departament d'Educació - Generalitat de Catalunya
 - 
EN
EN

"Let's think about our presentations: besides being informative, are they engaging and motivating?"  Carme will give us some hints about how to improve our scientific oral presentations and how to make them live and motivating for the audience.

Carme Roig is in charge of educational and technological innovation at STBCO, Department of Education, Catalonia, Spain. She has a degree in English Philology (UB, 1986) and a Master’s degree in TESOL (Institute of Education, UCL, 1996). She has coordinated a team of teachers who work to introduce and promote cooperative learning practices in high schools as members of XCB (Xarxa de Competències Bàsiques). In recent years, she has been working with several research groups (Goldsmiths College, London, University of Ghent, Belgium, SONY Labs, Paris, IIIA-CSIC Bellaterra) on issues of collaborative distance learning, self-assessment and co-assessment, and participated in experiments to validate a number of computer tools developed within the framework of a European project and in collaboration with the Research Institute for Artificial Intelligence, IIIA, CSIC. These tools include the automatic design of lesson plans, assessment tools to assess large numbers of students (MOOC) and tools for students’ grouping. Her main interests are Automated team formation, collaborative work, formative assessment and multilingualism.

"Let's think about our presentations: besides being informative, are they engaging and motivating?"  Carme will give us some hints about how to improve our scientific oral presentations and how to make them live and motivating for the audience.

Carme Roig is in charge of educational and technological innovation at STBCO, Department of Education, Catalonia, Spain. She has a degree in English Philology (UB, 1986) and a Master’s degree in TESOL (Institute of Education, UCL, 1996). She has coordinated a team of teachers who work to introduce and promote cooperative learning practices in high schools as members of XCB (Xarxa de Competències Bàsiques). In recent years, she has been working with several research groups (Goldsmiths College, London, University of Ghent, Belgium, SONY Labs, Paris, IIIA-CSIC Bellaterra) on issues of collaborative distance learning, self-assessment and co-assessment, and participated in experiments to validate a number of computer tools developed within the framework of a European project and in collaboration with the Research Institute for Artificial Intelligence, IIIA, CSIC. These tools include the automatic design of lesson plans, assessment tools to assess large numbers of students (MOOC) and tools for students’ grouping. Her main interests are Automated team formation, collaborative work, formative assessment and multilingualism.

 
16/Mar/2021
16/Mar/2021

Realtime analysis of fluorescence imaging data
Realtime analysis of fluorescence imaging data

Andrea Giovanucci
Andrea Giovanucci
 - 
Neural Engineering Laboratory, UNC Chapel-Hill
Neural Engineering Laboratory, UNC Chapel-Hill
 - 
EN
EN

Optical imaging methods using fluorescence indicators are critical for monitoring the activity of large neuronal populations in vivo. Imaging experiments typically generate a large amount of data that needs to be processed to extract the activity of the imaged neuronal sources. While deriving such processing algorithms is an active area of research, most existing methods require the processing of large amounts of data at a time, rendering them vulnerable to the volume of the recorded data, and preventing real-time experimental interrogation. In this talk I will describe CaImAn Online, a framework for the analysis of streaming calcium imaging data, including i) motion artifact correction, ii) neuronal source extraction, and iii) activity denoising and deconvolution. Our approach combines and extends previous work on online dictionary learning and calcium imaging data analysis, to deliver an automated pipeline that can discover and track the activity of hundreds of cells in real time, thereby enabling new types of closed-loop experiments.

Dr. Andrea Giovannucci is an Assistant Professor in Neural Engineering at the UNC/NCSU department of Bioengineering. Prior to this appointment, Dr. Giovannucci was a machine learning data scientist at the Flatiron Institute (Simons Foundation) and a postdoctoral fellow (experimental neuroscience) at the Princeton Neuroscience Institute. Dr. Giovannucci obtained his PhD in artificial intelligence from the Autonoma University of Barcelona and the Artificial Intelligence Research Institute of Bellaterra (IIIA-CSIC), Spain. Dr. Giovannucci is affiliated with the UNC/NCSU joint Bioengineering department, the Closed-loop Engineering for Advanced Rehabilitation (CLEAR) and the UNC Neuroscience Center.

Optical imaging methods using fluorescence indicators are critical for monitoring the activity of large neuronal populations in vivo. Imaging experiments typically generate a large amount of data that needs to be processed to extract the activity of the imaged neuronal sources. While deriving such processing algorithms is an active area of research, most existing methods require the processing of large amounts of data at a time, rendering them vulnerable to the volume of the recorded data, and preventing real-time experimental interrogation. In this talk I will describe CaImAn Online, a framework for the analysis of streaming calcium imaging data, including i) motion artifact correction, ii) neuronal source extraction, and iii) activity denoising and deconvolution. Our approach combines and extends previous work on online dictionary learning and calcium imaging data analysis, to deliver an automated pipeline that can discover and track the activity of hundreds of cells in real time, thereby enabling new types of closed-loop experiments.

Dr. Andrea Giovannucci is an Assistant Professor in Neural Engineering at the UNC/NCSU department of Bioengineering. Prior to this appointment, Dr. Giovannucci was a machine learning data scientist at the Flatiron Institute (Simons Foundation) and a postdoctoral fellow (experimental neuroscience) at the Princeton Neuroscience Institute. Dr. Giovannucci obtained his PhD in artificial intelligence from the Autonoma University of Barcelona and the Artificial Intelligence Research Institute of Bellaterra (IIIA-CSIC), Spain. Dr. Giovannucci is affiliated with the UNC/NCSU joint Bioengineering department, the Closed-loop Engineering for Advanced Rehabilitation (CLEAR) and the UNC Neuroscience Center.

 
09/Mar/2021
09/Mar/2021

TES X-ray pulse identification using CNNs
TES X-ray pulse identification using CNNs

Jesús Vega Ferrero
Jesús Vega Ferrero
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

Transition Edge Sensors (TES) detector devices, like the one that will be onboard the Athena X-ray Observatory, produce current pulses as a response to the incident X-ray photons. The reconstruction of these pulses aims at recovering the energy of the impacting photon, its arrival time and its physical position in the detector. This has been traditionally performed by means of a triggering algorithm based on the derivative signal overcoming a threshold (detection) followed by optimal filtering (to retrieve the energy of each event). However, when the arrival of the photons is very close in time, the triggering algorithm is incapable of detecting all the individual pulses. Aiming at improving the efficiency of the detection process, we use an alternative approach with Machine Learning techniques. For this purpose, we construct and train a series of Neural Networks (NNs) not only for the detection but also to recover the energy of simulated X-ray pulses. The dataset used to train the NNs consists of simulations performed with SIXTE/xifusim, the Athena/X-IFU official simulator. Although much expensive in terms of computational cost, the performance of our classification NN clearly surpasses the detection performance of the classical triggering approach for the full range of photon energy combinations showing excellent metrics. The reconstruction efficiency for the recovery of the energy of the photons cannot however currently compete with the optimal filtering algorithm.

Transition Edge Sensors (TES) detector devices, like the one that will be onboard the Athena X-ray Observatory, produce current pulses as a response to the incident X-ray photons. The reconstruction of these pulses aims at recovering the energy of the impacting photon, its arrival time and its physical position in the detector. This has been traditionally performed by means of a triggering algorithm based on the derivative signal overcoming a threshold (detection) followed by optimal filtering (to retrieve the energy of each event). However, when the arrival of the photons is very close in time, the triggering algorithm is incapable of detecting all the individual pulses. Aiming at improving the efficiency of the detection process, we use an alternative approach with Machine Learning techniques. For this purpose, we construct and train a series of Neural Networks (NNs) not only for the detection but also to recover the energy of simulated X-ray pulses. The dataset used to train the NNs consists of simulations performed with SIXTE/xifusim, the Athena/X-IFU official simulator. Although much expensive in terms of computational cost, the performance of our classification NN clearly surpasses the detection performance of the classical triggering approach for the full range of photon energy combinations showing excellent metrics. The reconstruction efficiency for the recovery of the energy of the photons cannot however currently compete with the optimal filtering algorithm.

 
02/Mar/2021
02/Mar/2021

Complex Networks Generation: From Probabilistic Models to Deep Generative Approaches
Complex Networks Generation: From Probabilistic Models to Deep Generative Approaches

Jesús Giráldez Crú
Jesús Giráldez Crú
 - 
Andalusian Research Institute DaSCI, University of Granada
Andalusian Research Institute DaSCI, University of Granada
 - 
EN
EN

Complex networks are ubiquitous to represent real systems in many contexts, such as social networks, computer networks, or biological networks, among others. Most of the real-world networks exhibit non-trivial topological features, and the interest in analyzing their properties has resulted in the emergence of random models to generate them. Probabilistic models are, in general, based on the probability of each edge to occur, and the topology of the network is the consequence of such a probability distribution. In deep generative approaches, a model is trained to learn the features of a training set of examples and generate new networks with similar properties. In this seminar we will review a (non-exhaustive) list of random models of complex networks generation, and analyze how these models can be applied to another challenging problem: the generation of realistic random SAT instances.

Complex networks are ubiquitous to represent real systems in many contexts, such as social networks, computer networks, or biological networks, among others. Most of the real-world networks exhibit non-trivial topological features, and the interest in analyzing their properties has resulted in the emergence of random models to generate them. Probabilistic models are, in general, based on the probability of each edge to occur, and the topology of the network is the consequence of such a probability distribution. In deep generative approaches, a model is trained to learn the features of a training set of examples and generate new networks with similar properties. In this seminar we will review a (non-exhaustive) list of random models of complex networks generation, and analyze how these models can be applied to another challenging problem: the generation of realistic random SAT instances.

 
23/Feb/2021
23/Feb/2021

Adding Negative Learning to Ant ColonyOptimization: A Comprehensive Study
Adding Negative Learning to Ant ColonyOptimization: A Comprehensive Study

Teddy Nurcahyadi
Teddy Nurcahyadi
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this talk I present an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. The study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this talk I present an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. The study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

 
16/Feb/2021
16/Feb/2021

Learning Natural Languages by Learning Matrices (a brief overview)
Learning Natural Languages by Learning Matrices (a brief overview)

Xavier Carreras
Xavier Carreras
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

Natural Language Understanding (NLU) is the broad research area in Natural Language Processing (NLP) that develops methods to analyze natural language and understand its meaning. It is a key component of any AI system that aims at truly interacting with humans. It is also a key component for automatic systems that do machine reading of the web and social media, which, given the current volumes of information, is the only practical way to access this content.

First I will give a brief overview of Natural Processing Processing tasks, and the evolution of machine learning approaches in recent years. Natural language is structured, very rich, ambiguous, and offers limitless ability to say new things. Because of this, the desire is to have machine learning algorithms that learn hidden-state compositional models of language, and answer questions such as: what are the units and parts of a language? what is the meaning of each part? how do we compose parts into bigger parts? what is the meaning of a composed expression? how do we use these models to solve specific needs? 

Deep learning has made great progress on these questions. Today we have giant neural models like BERT or GPT-3 that are trained at worldwide scale, and are found useful for virtually any empirical NLP task. However, it's largely unclear what these models are learning, and what is their capacity to generalize (as opposed to memorizing data). Also, the costs of learning these models is huge. 

In the second part of this talk, I will focus on compositional models of language that take the form of weighted automata, which are a restricted class of recurrent neural networks. I will describe Spectral Learning algorithms, a family of learning algorithms that reduces the problem of learning a weighted automata to some form of matrix learning. This reduction is based on theoretical connections between formal languages and distributions over the strings they generate. I will highlight several good properties of this family of techniques, and contrast them with deep learning approaches.

Finally, I will describe some research lines on unsupervised spectral learning of natural language grammars that I will pursue in the next few years. 

Natural Language Understanding (NLU) is the broad research area in Natural Language Processing (NLP) that develops methods to analyze natural language and understand its meaning. It is a key component of any AI system that aims at truly interacting with humans. It is also a key component for automatic systems that do machine reading of the web and social media, which, given the current volumes of information, is the only practical way to access this content.

First I will give a brief overview of Natural Processing Processing tasks, and the evolution of machine learning approaches in recent years. Natural language is structured, very rich, ambiguous, and offers limitless ability to say new things. Because of this, the desire is to have machine learning algorithms that learn hidden-state compositional models of language, and answer questions such as: what are the units and parts of a language? what is the meaning of each part? how do we compose parts into bigger parts? what is the meaning of a composed expression? how do we use these models to solve specific needs? 

Deep learning has made great progress on these questions. Today we have giant neural models like BERT or GPT-3 that are trained at worldwide scale, and are found useful for virtually any empirical NLP task. However, it's largely unclear what these models are learning, and what is their capacity to generalize (as opposed to memorizing data). Also, the costs of learning these models is huge. 

In the second part of this talk, I will focus on compositional models of language that take the form of weighted automata, which are a restricted class of recurrent neural networks. I will describe Spectral Learning algorithms, a family of learning algorithms that reduces the problem of learning a weighted automata to some form of matrix learning. This reduction is based on theoretical connections between formal languages and distributions over the strings they generate. I will highlight several good properties of this family of techniques, and contrast them with deep learning approaches.

Finally, I will describe some research lines on unsupervised spectral learning of natural language grammars that I will pursue in the next few years. 

 
02/Feb/2021
02/Feb/2021

Games with Negotiations
Games with Negotiations

Dave de Jonge
Dave de Jonge
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

Classical solution concepts in game theory, such as the Nash equilibrium and the subgame-perfect equilibrium, are based on the assumption that players make their choices on a purely individual basis and that they are not able to coordinate their actions through binding agreements. This sometimes yields counter-intuitive results, such as in the Prisoner's Dilemma. In most real-world situations that are similar to the Prisoner's dilemma, people can negotiate and jointly agree to choose their actions in a way that prevents them from hurting each other. If necessary, with the help of legally binding contracts.

In this talk I will therefore introduce a new game-theoretical solution concept that does take into account the possibility for the players to make binding agreements about their actions. I will use a classical text-book game known as the Centipede Game as an example, and show how this new solution concept prescribes a more satisfactory outcome than the classical subgame-perfect equilibrium. Furthermore, I will present experimental results obtained with a negotiation algorithm based on Monte Carlo Tree Search.  

Classical solution concepts in game theory, such as the Nash equilibrium and the subgame-perfect equilibrium, are based on the assumption that players make their choices on a purely individual basis and that they are not able to coordinate their actions through binding agreements. This sometimes yields counter-intuitive results, such as in the Prisoner's Dilemma. In most real-world situations that are similar to the Prisoner's dilemma, people can negotiate and jointly agree to choose their actions in a way that prevents them from hurting each other. If necessary, with the help of legally binding contracts.

In this talk I will therefore introduce a new game-theoretical solution concept that does take into account the possibility for the players to make binding agreements about their actions. I will use a classical text-book game known as the Centipede Game as an example, and show how this new solution concept prescribes a more satisfactory outcome than the classical subgame-perfect equilibrium. Furthermore, I will present experimental results obtained with a negotiation algorithm based on Monte Carlo Tree Search.  

 
26/Jan/2021
26/Jan/2021

Optimization: an important tool in research and in industry
Optimization: an important tool in research and in industry

Christian Blum
Christian Blum
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

In this talk I will first give a short overview on optimization and on the related topics that have been subject of our work during the last years. In the second part of the talk I will report on an industrial project that we conducted in 2020 in cooperation with IKERLAN S. Coop. in the context of the optimization of safety-critical systems.

In this talk I will first give a short overview on optimization and on the related topics that have been subject of our work during the last years. In the second part of the talk I will report on an industrial project that we conducted in 2020 in cooperation with IKERLAN S. Coop. in the context of the optimization of safety-critical systems.

 
19/Jan/2021
19/Jan/2021

Current trends in CV: A few practical examples
Current trends in CV: A few practical examples

Fernando Vilariño
Fernando Vilariño
 - 
CVC
CVC
 - 
EN
EN

Computer Vision has become one of the most relevant fields of work in AI. During recent years, and with the explosion of Deep Learning and the possibility to have access to massive data sets, Computer Vision itself has also become one of the main driving forces of the AI market, with multiple applications in different areas of social impact such as autonomous mobility, health and well-being, intelligent media analysis, industry 4.0, etc. Tools such as Convolutional Neural Networks have become prominent and omnipresent in approaches tackling both general and specific problems, and the pace at which these new solutions are appearing, day after day, is changing the Computer Vision research scenario dramatically. In this seminar, Prof. Fernando Vilariño (Associate Director and Group Responsible for Research Projects at Computer Vision Centre (CVC) (http://www.cvc.uab.es/)) will provide an introduction to the main areas of impact tackled by the Computer Vision Center, by introducing a number of paradigmatic examples of Computer Vision-based projects, putting emphasis on the specific techniques used. The presentation will have a very practical approach and will allow those interested in deepening in the Computer Vision field to have a set of pointers to dig in, both from a purely scientific or a more implementation-oriented perspective.

Computer Vision has become one of the most relevant fields of work in AI. During recent years, and with the explosion of Deep Learning and the possibility to have access to massive data sets, Computer Vision itself has also become one of the main driving forces of the AI market, with multiple applications in different areas of social impact such as autonomous mobility, health and well-being, intelligent media analysis, industry 4.0, etc. Tools such as Convolutional Neural Networks have become prominent and omnipresent in approaches tackling both general and specific problems, and the pace at which these new solutions are appearing, day after day, is changing the Computer Vision research scenario dramatically. In this seminar, Prof. Fernando Vilariño (Associate Director and Group Responsible for Research Projects at Computer Vision Centre (CVC) (http://www.cvc.uab.es/)) will provide an introduction to the main areas of impact tackled by the Computer Vision Center, by introducing a number of paradigmatic examples of Computer Vision-based projects, putting emphasis on the specific techniques used. The presentation will have a very practical approach and will allow those interested in deepening in the Computer Vision field to have a set of pointers to dig in, both from a purely scientific or a more implementation-oriented perspective.

 
15/Dec/2020
15/Dec/2020

The new CAP-IA of the IIIA
The new CAP-IA of the IIIA

Evili del Rio
Evili del Rio
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

Presentation of the High-Performance Cluster for Artificial Intelligence of the IIIA: Technical characteristics, rules of use and operation, available software and mini user guide.

[This is an internal webinar for people working at the IIIA-CSIC]

Presentation of the High-Performance Cluster for Artificial Intelligence of the IIIA: Technical characteristics, rules of use and operation, available software and mini user guide.

[This is an internal webinar for people working at the IIIA-CSIC]

 
01/Dec/2020
01/Dec/2020

CorporIS: Theory and Computation of Embodied Conceptualisation for Information Systems
CorporIS: Theory and Computation of Embodied Conceptualisation for Information Systems

Marco Schorlemmer
Marco Schorlemmer
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

We will provide a brief overview of the CorporIS project, funded by Spain’s Ministerio de Ciencia e Innovación. The project aims at contributing to the conceptual and theoretical foundations for a mathematical and computational model of embodied conceptualisation, driven by its potential deployment and application in cognitive musicology and musical creativity.

We will provide a brief overview of the CorporIS project, funded by Spain’s Ministerio de Ciencia e Innovación. The project aims at contributing to the conceptual and theoretical foundations for a mathematical and computational model of embodied conceptualisation, driven by its potential deployment and application in cognitive musicology and musical creativity.

 
24/Nov/2020
24/Nov/2020

What can AI tell us about Creativity, Music Improvisation and Learning?
What can AI tell us about Creativity, Music Improvisation and Learning?

Mark d'Inverno
Mark d'Inverno
 - 
Goldsmiths, University of London
Goldsmiths, University of London
 - 
EN
EN

Prof. Mark d'Inverno: "Using a piano (I hope), AI software and a few videos I will aim to try and answer this question from the perspectives of researcher, musician and lecturer."

Bio:

Mark d'Inverno has spent the last 20 years undertaking cutting-edge research at the frontiers of AI, creativity and learning –luckily for him that much has been with colleagues at IIIA - asking how they relate to each other and how the different academic disciplines can provide us with insights into the role we want AI to play in learning, in creative practice and in society in general. Mark's PhD from UCL investigated the concepts of agency and autonomy in artificial systems, and since then he has published around peer-reviewed 200 articles and several books (including the edited book "Computers and Creativity"). Mark was formerly Pro-Warden (Pro-Vice-Chancellor) at Goldsmiths, University of London - known for an array of alumni who have contributed to the creative and cultural industries nationally and internationally - where he has led on developing the College's international profile and engagement and before that led the research and enterprise brief. He is a critically acclaimed jazz pianist (Guardian, Observer, BBC) and for nearly 40 years has led a variety of successful bands in a range of different musical genres.

Prof. Mark d'Inverno: "Using a piano (I hope), AI software and a few videos I will aim to try and answer this question from the perspectives of researcher, musician and lecturer."

Bio:

Mark d'Inverno has spent the last 20 years undertaking cutting-edge research at the frontiers of AI, creativity and learning –luckily for him that much has been with colleagues at IIIA - asking how they relate to each other and how the different academic disciplines can provide us with insights into the role we want AI to play in learning, in creative practice and in society in general. Mark's PhD from UCL investigated the concepts of agency and autonomy in artificial systems, and since then he has published around peer-reviewed 200 articles and several books (including the edited book "Computers and Creativity"). Mark was formerly Pro-Warden (Pro-Vice-Chancellor) at Goldsmiths, University of London - known for an array of alumni who have contributed to the creative and cultural industries nationally and internationally - where he has led on developing the College's international profile and engagement and before that led the research and enterprise brief. He is a critically acclaimed jazz pianist (Guardian, Observer, BBC) and for nearly 40 years has led a variety of successful bands in a range of different musical genres.

 
17/Nov/2020
17/Nov/2020

The UKRI Trustworthy Autonomous Systems Hub
The UKRI Trustworthy Autonomous Systems Hub

Gopal Ramchurn
Gopal Ramchurn
 - 
University of Southampton
University of Southampton
 - 
EN
EN

Professor Gopal Ramchurn from the University of Southampton will give us a brief overview of some of the latest research he has carried out in the area of human-agent collectives and will articulate some of the key challenges that arise when building AI needs to be trustworthy by design and trusted in practice. Then he will detail the programme of the UKRI Trustworthy Autonomous Systems Hub (www.tas.ac.uk), which is a newly funded £12m programme to coordinate a portfolio of £21m of research projects across multiple universities in the UK.

 

Professor Gopal Ramchurn from the University of Southampton will give us a brief overview of some of the latest research he has carried out in the area of human-agent collectives and will articulate some of the key challenges that arise when building AI needs to be trustworthy by design and trusted in practice. Then he will detail the programme of the UKRI Trustworthy Autonomous Systems Hub (www.tas.ac.uk), which is a newly funded £12m programme to coordinate a portfolio of £21m of research projects across multiple universities in the UK.

 

 
03/Nov/2020
03/Nov/2020

Approximately Equivalent Induced Subgraph Games
Approximately Equivalent Induced Subgraph Games

Filippo Bistaffa
Filippo Bistaffa
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

Speaker: Filippo Bistaffa - researcher at the IIIA-CSIC. 

Filippo will present an approach that allows one to approximate every characteristic function games (CFG) as an induced subgraph game (ISG), a succinct game representation that is based on a weighted graph among the agents. The proposal outperforms existing CSG approaches for ISGs by using off-the-shelf optimisation solvers.

Speaker: Filippo Bistaffa - researcher at the IIIA-CSIC. 

Filippo will present an approach that allows one to approximate every characteristic function games (CFG) as an induced subgraph game (ISG), a succinct game representation that is based on a weighted graph among the agents. The proposal outperforms existing CSG approaches for ISGs by using off-the-shelf optimisation solvers.

 
27/Oct/2020
27/Oct/2020

A Deep Vision of Lifelong Learning Multilingual Machine Translation
A Deep Vision of Lifelong Learning Multilingual Machine Translation

Marta R. Costa-Jussà
Marta R. Costa-Jussà
 - 
IDEAI-UPC
IDEAI-UPC
 - 
EN
EN

Marta R. Costa-jussà, a Ramon y Cajal Researcher at the Universitat Politècnica de Catalunya (UPC, Barcelona), will be giving some deep insights into (spoken) multilingual language translation pursuing similar quality for all languages. Also, we are going to discuss how we can efficiently add new languages in a highly multilingual system. Finally, we are going to give details on the fairness challenge, why neutral words as “doctor” tend to infer the “male” gender when translated into a language that requires gender flexion for this word?

Marta R. Costa-jussà, a Ramon y Cajal Researcher at the Universitat Politècnica de Catalunya (UPC, Barcelona), will be giving some deep insights into (spoken) multilingual language translation pursuing similar quality for all languages. Also, we are going to discuss how we can efficiently add new languages in a highly multilingual system. Finally, we are going to give details on the fairness challenge, why neutral words as “doctor” tend to infer the “male” gender when translated into a language that requires gender flexion for this word?

 
20/Oct/2020
20/Oct/2020

AI4EU: A European AI On Demand Platform and Ecosystem
AI4EU: A European AI On Demand Platform and Ecosystem

Juan Antonio Rodríguez
Juan Antonio Rodríguez
 - 
IIIA-CSIC
IIIA-CSIC
 - 
EN
EN

Professor Juan Antonio Rodríguez will present to us the AI4EU project. AI4EU is the European Union’s landmark Artificial Intelligence project, which seeks to develop a European AI ecosystem, bringing together the knowledge, algorithms, tools and resources available and making it a compelling solution for users. Involving 80 partners, covering 21 countries, the €20m project kicked off in January 2019 and will run for three years. 

Professor Juan Antonio Rodríguez will present to us the AI4EU project. AI4EU is the European Union’s landmark Artificial Intelligence project, which seeks to develop a European AI ecosystem, bringing together the knowledge, algorithms, tools and resources available and making it a compelling solution for users. Involving 80 partners, covering 21 countries, the €20m project kicked off in January 2019 and will run for three years. 

 
14/Oct/2020
14/Oct/2020

Tres años de experiencia en IDEAI-UPC: nuevos proyectos, nuevas sinergias
Tres años de experiencia en IDEAI-UPC: nuevos proyectos, nuevas sinergias

Cecilio Angulo
Cecilio Angulo
 - 
IDEAI-UPC
IDEAI-UPC
 - 
SP
SP

Professor Cecilio Angulo will present us the IDEAI-UPC (https://ideai.upc.edu/en) research lab. Cecilio is the current director of the IDEAI-UPC. He will present some of the projects that are being carried out at the IDEAI and that perhaps may open up new collaboration lines between the two institutes.

Professor Cecilio Angulo will present us the IDEAI-UPC (https://ideai.upc.edu/en) research lab. Cecilio is the current director of the IDEAI-UPC. He will present some of the projects that are being carried out at the IDEAI and that perhaps may open up new collaboration lines between the two institutes.