108 research outputs found
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping
The proliferation and ubiquity of temporal data across many disciplines has
sparked interest for similarity, classification and clustering methods
specifically designed to handle time series data. A core issue when dealing
with time series is determining their pairwise similarity, i.e., the degree to
which a given time series resembles another. Traditional distance measures such
as the Euclidean are not well-suited due to the time-dependent nature of the
data. Elastic metrics such as dynamic time warping (DTW) offer a promising
approach, but are limited by their computational complexity,
non-differentiability and sensitivity to noise and outliers. This thesis
proposes novel elastic alignment methods that use parametric \& diffeomorphic
warping transformations as a means of overcoming the shortcomings of DTW-based
metrics. The proposed method is differentiable \& invertible, well-suited for
deep learning architectures, robust to noise and outliers, computationally
efficient, and is expressive and flexible enough to capture complex patterns.
Furthermore, a closed-form solution was developed for the gradient of these
diffeomorphic transformations, which allows an efficient search in the
parameter space, leading to better solutions at convergence. Leveraging the
benefits of these closed-form diffeomorphic transformations, this thesis
proposes a suite of advancements that include: (a) an enhanced temporal
transformer network for time series alignment and averaging, (b) a
deep-learning based time series classification model to simultaneously align
and classify signals with high accuracy, (c) an incremental time series
clustering algorithm that is warping-invariant, scalable and can operate under
limited computational and time resources, and finally, (d) a normalizing flow
model that enhances the flexibility of affine transformations in coupling and
autoregressive layers.Comment: PhD Thesis, defended at the University of Navarra on July 17, 2023.
277 pages, 8 chapters, 1 appendi
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
The quintessential model-based reinforcement-learning agent iteratively
refines its estimates or prior beliefs about the true underlying model of the
environment. Recent empirical successes in model-based reinforcement learning
with function approximation, however, eschew the true model in favor of a
surrogate that, while ignoring various facets of the environment, still
facilitates effective planning over behaviors. Recently formalized as the value
equivalence principle, this algorithmic technique is perhaps unavoidable as
real-world reinforcement learning demands consideration of a simple,
computationally-bounded agent interacting with an overwhelmingly complex
environment, whose underlying dynamics likely exceed the agent's capacity for
representation. In this work, we consider the scenario where agent limitations
may entirely preclude identifying an exactly value-equivalent model,
immediately giving rise to a trade-off between identifying a model that is
simple enough to learn while only incurring bounded sub-optimality. To address
this problem, we introduce an algorithm that, using rate-distortion theory,
iteratively computes an approximately-value-equivalent, lossy compression of
the environment which an agent may feasibly target in lieu of the true model.
We prove an information-theoretic, Bayesian regret bound for our algorithm that
holds for any finite-horizon, episodic sequential decision-making problem.
Crucially, our regret bound can be expressed in one of two possible forms,
providing a performance guarantee for finding either the simplest model that
achieves a desired sub-optimality gap or, alternatively, the best model given a
limit on agent capacity.Comment: Accepted to Neural Information Processing Systems (NeurIPS) 202
Advances in Computational Intelligence Applications in the Mining Industry
This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners
Complexity in Economic and Social Systems
There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure
Information Theory and Machine Learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems
Artificial Neural Networks in Agriculture
Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible
History, Man, and Reason
Originally published in 1971. The purpose of this book is to draw attention to important aspects of thought in the nineteenth century. While its central concerns lie within the philosophic tradition, materials drawn from the social sciences and elsewhere provide important illustrations of the intellectual movements that the author attempts to trace. This book aims at examining philosophic modes of thought as well as sifting presuppositions held in common by a diverse group of thinkers whose antecedents and whose intentions often had little in common. After a preliminary tracing of the main strands of continuity within philosophy itself, the author concentrates on how, out of diverse and disparate sources, certain common beliefs and attitudes regarding history, man, and reason came to pervade a great deal of nineteenth-century thought. Geographically, this book focuses on English, French, and German thought. Mandelbaum believes that views regarding history and man and reason pose problems for philosophy, and he offers critical discussions of some of those problems at the conclusions of parts 2, 3, and 4
Advances in Data Mining Knowledge Discovery and Applications
Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications
Corpus linguistics: A guide to the methodology
Corpora are widely used in linguistics, but not always wisely. This book attempts to frame corpus linguistics systematically as a variant of the observational method. The first part introduces the reader to the general methodological discussions surrounding corpus data as well as the practice of doing corpus linguistics, including issues such as the scientific research cycle, research design, extraction of corpus data and statistical evaluation. The second part consists of a number of case studies from the main areas of corpus linguistics (lexical associations, morphology, grammar, text and metaphor), surveying the range of issues studied in corpus linguistics while at the same time showing how they fit into the methodology outlined in the first part
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