699 research outputs found
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
Graceful Degradation and Related Fields
When machine learning models encounter data which is out of the distribution
on which they were trained they have a tendency to behave poorly, most
prominently over-confidence in erroneous predictions. Such behaviours will have
disastrous effects on real-world machine learning systems. In this field
graceful degradation refers to the optimisation of model performance as it
encounters this out-of-distribution data. This work presents a definition and
discussion of graceful degradation and where it can be applied in deployed
visual systems. Following this a survey of relevant areas is undertaken,
novelly splitting the graceful degradation problem into active and passive
approaches. In passive approaches, graceful degradation is handled and achieved
by the model in a self-contained manner, in active approaches the model is
updated upon encountering epistemic uncertainties. This work communicates the
importance of the problem and aims to prompt the development of machine
learning strategies that are aware of graceful degradation
Análisis de datos etnográficos, antropológicos y arqueológicos: una aproximación desde las humanidades digitales y los sistemas complejos
La llegada de las Ciencias de la Computación, el Big Data, el Análisis de Datos, el Aprendizaje Automático y la Minería de Datos ha modificado la manera en que se hace ciencia en todos los campos científicos, dando lugar, a su vez, a la aparición de nuevas disciplinas tales como la Mecánica Computacional, la Bioinformática, la Ingeniería de la Salud, las Ciencias Sociales Computacionales, la Economía Computacional, la Arqueología Computacional y las Humanidades Digitales –entre otras. Cabe destacar que todas estas nuevas disciplinas son todavía muy jóvenes y están en continuo crecimiento, por lo que contribuir a su avance y consolidación tiene un gran valor científico.
En esta tesis doctoral contribuimos al desarrollo de una nueva línea de investigación dedicada al uso de modelos formales, métodos analíticos y enfoques computacionales para el estudio de las sociedades humanas tanto actuales como del pasado.El Ministerio de Ciencia e Innovación
• Proyecto SimulPast – “Transiciones sociales y ambientales: simulando el pasado para
entender el comportamiento humano” (CSD2010-00034 CONSOLIDER-INGENIO
2010).
• Proyecto CULM – “Modelado del cultivo en la prehistoria” (HAR2016-77672-P).
• Red de Excelencia SimPastNet – “Simular el pasado para entender el
comportamiento humano” (HAR2017-90883-REDC).
• Red de Excelencia SocioComplex – “Sistemas Complejos Socio-Tecnológicos”
(RED2018-102518-T).
La Consejería de Educación de la Junta de Castilla y León
• Subvención a la línea de investigación “Entendiendo el comportamiento humano,
una aproximación desde los sistemas complejos y las humanidades digitales” dentro
del programa de apoyo a los grupos de investigación reconocidos (GIR) de las
universidades públicas de Castilla y León (BDNS 425389
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The resurgence of structure in deep neural networks
Machine learning with deep neural networks ("deep learning") allows for learning complex features directly from raw input data, completely eliminating hand-crafted, "hard-coded" feature extraction from the learning pipeline. This has lead to state-of-the-art performance being achieved across several---previously disconnected---problem domains, including computer vision, natural language processing, reinforcement learning and generative modelling. These success stories nearly universally go hand-in-hand with availability of immense quantities of labelled training examples ("big data") exhibiting simple grid-like structure (e.g. text or images), exploitable through convolutional or recurrent layers. This is due to the extremely large number of degrees-of-freedom in neural networks, leaving their generalisation ability vulnerable to effects such as overfitting.
However, there remain many domains where extensive data gathering is not always appropriate, affordable, or even feasible. Furthermore, data is generally organised in more complicated kinds of structure---which most existing approaches would simply discard. Examples of such tasks are abundant in the biomedical space; with e.g. small numbers of subjects available for any given clinical study, or relationships between proteins specified via interaction networks. I hypothesise that, if deep learning is to reach its full potential in such environments, we need to reconsider "hard-coded" approaches---integrating assumptions about inherent structure in the input data directly into our architectures and learning algorithms, through structural inductive biases. In this dissertation, I directly validate this hypothesis by developing three structure-infused neural network architectures (operating on sparse multimodal and graph-structured data), and a structure-informed learning algorithm for graph neural networks, demonstrating significant outperformance of conventional baseline models and algorithms.The work depicted in this dissertation was in part supported by funding from the European Union's Horizon 2020 research and innovation programme PROPAG-AGEING under grant agreement No 634821
A Defense of Pure Connectionism
Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming increasingly clear, in practice) the resources to model even the most rich and distinctly human cognitive capacities, such as abstract, conceptual thought and natural language comprehension and production.
Consonant with much previous philosophical work on connectionism, I argue that a core principle—that proximal representations in a vector space have similar semantic values—is the key to a successful connectionist account of the systematicity and productivity of thought, language, and other core cognitive phenomena. My work here differs from preceding work in philosophy in several respects: (1) I compare a wide variety of connectionist responses to the systematicity challenge and isolate two main strands that are both historically important and reflected in ongoing work today: (a) vector symbolic architectures and (b) (compositional) vector space semantic models; (2) I consider very recent applications of these approaches, including their deployment on large-scale machine learning tasks such as machine translation; (3) I argue, again on the basis mostly of recent developments, for a continuity in representation and processing across natural language, image processing and other domains; (4) I explicitly link broad, abstract features of connectionist representation to recent proposals in cognitive science similar in spirit, such as hierarchical Bayesian and free energy minimization approaches, and offer a single rebuttal of criticisms of these related paradigms; (5) I critique recent alternative proposals that argue for a hybrid Classical (i.e. serial symbolic)/statistical model of mind; (6) I argue that defending the most plausible form of a connectionist cognitive architecture requires rethinking certain distinctions that have figured prominently in the history of the philosophy of mind and language, such as that between word- and phrase-level semantic content, and between inference and association
A Unified Framework for Gradient-based Hyperparameter Optimization and Meta-learning
Machine learning algorithms and systems are progressively becoming part of our societies, leading to a growing need of building a vast multitude of accurate, reliable and interpretable models which should possibly exploit similarities among tasks. Automating segments of machine learning itself seems to be a natural step to undertake to deliver increasingly capable systems able to perform well in both the big-data and the few-shot learning regimes. Hyperparameter optimization (HPO) and meta-learning (MTL) constitute two building blocks of this growing effort. We explore these two topics under a unifying perspective, presenting a mathematical framework linked to bilevel programming that captures existing similarities and translates into procedures of practical interest rooted in algorithmic differentiation. We discuss the derivation, applicability and computational complexity of these methods and establish several approximation properties for a class of objective functions of the underlying bilevel programs. In HPO, these algorithms generalize and extend previous work on gradient-based methods. In MTL, the resulting framework subsumes classic and emerging strategies and provides a starting basis from which to build and analyze novel techniques. A series of examples and numerical simulations offer insight and highlight some limitations of these approaches. Experiments on larger-scale problems show the potential gains of the proposed methods in real-world applications. Finally, we develop two extensions of the basic algorithms apt to optimize a class of discrete hyperparameters (graph edges) in an application to relational learning and to tune online learning rate schedules for training neural network models, an old but crucially important issue in machine learning
More is Different: Modern Computational Modeling for Heterogeneous Catalysis
La combinació d'observacions experimentals i estudis de la Density Functional Theory (DFT) és un dels pilars de la
investigació química moderna. Atès que permeten recopilar informació física addicional d'un sistema químic,
difícilment accessible a través de l'entorn experimental, aquests estudis es fan servir àmpliament per modelar i predir
el comportament d'una gran varietat de compostos químics en entorns únics. A la catàlisi heterogènia, els models
DFT s'utilitzen habitualment per avaluar la interacció entre els compostos moleculars i els catalitzadors, vinculant
aquestes interpretacions amb els resultats experimentals. Tanmateix, l'alta complexitat trobada tant als escenaris
catalítics com a la reactivitat, implica la necessitat de metodologies sofisticades que requereixen automatització,
emmagatzematge i anàlisi per estudiar correctament aquests sistemes. Aquest treball presenta el desenvolupament i
la combinació de múltiples metodologies per avaluar correctament la complexitat d'aquests sistemes químics. A més,
aquest treball mostra com s'han utilitzat les tècniques proporcionades per estudiar noves configuracions catalítiques
d'interès acadèmic i industrial.La combinación de observaciones experimentales y estudios de la Density Functional Theory (DFT) es uno de los
pilares de la investigación química moderna. Dado que permiten recopilar información física adicional de un sistema
químico, difícilmente accesible a través del entorno experimental, estos estudios se emplean ampliamente para
modelar y predecir el comportamiento de una gran variedad de compuestos químicos en entornos únicos. En la
catálisis heterogénea, los modelos DFT se emplean habitualmente para evaluar la interacción entre los compuestos
moleculares y los catalizadores, vinculando estas interpretaciones con los resultados experimentales. Sin embargo, la
alta complejidad encontrada tanto en los escenarios catalíticos como en la reactividad, implica la necesidad de
metodologías sofisticadas que requieren de automatización, almacenamiento y análisis para estudiar correctamente
estos sistemas. Este trabajo presenta el desarrollo y la combinación de múltiples metodologías con el objetivo de
evaluar correctamente la complejidad de estos sistemas químicos. Además, este trabajo muestra cómo las técnicas
proporcionadas se han utilizado para estudiar nuevas configuraciones catalíticas de interés académico e industrial.The combination of Experimental observations and Density Functional Theory studies is one of the pillars of modern
chemical research. As they enable the collection of additional physical information of a chemical system, hardly
accessible via the experimental setting, Density Functional Theory studies are widely employed to model and predict
the behavior of a diverse variety of chemical compounds under unique environments. Particularly, in heterogeneous
catalysis, Density Functional Theory models are commonly employed to evaluate the interaction between molecular
compounds and catalysts, lately linking these interpretations with experimental results. However, high complexity
found in both, catalytic settings and reactivity, implies the need of sophisticated methodologies involving automation,
storage and analysis to correctly study these systems. Here, I present the development and combination of multiple
methodologies, aiming at correctly asses complexity. Also, this work shows how the provided techniques have been
actively used to study novel catalytic settings of academic and industrial interest
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