6,049 research outputs found

    Modular lifelong machine learning

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    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    Traditional Chinese State Ritual System of Sacrifice to Mountain and Water Spirits

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    This book focuses on the traditional Chinese ritual system of sacrifice to mountain and water spirits, a significant but largely overlooked sub-field of Chinse religious studies. This system mainly comprised the five sacred peaks, five strongholds, four seas, and four waterways, and was maintained for two thousand years in imperial China. As state ritual, it was constructed of by Confucian ritual culture, but in practice, it gradually interacted and integrated with various religious traditions, such as Daoism, Buddhism, and folk belief, especially in its local manifestation and dissemination. The eighteen great mountains and waters marked geographical and directional borders and territories modelled on the yin-yang and five-phase framework that helped shape Chinese people’s cosmographical understanding of the world. Together, they constituted a set of sacred spaces symbolizing the sanctioned political legitimacy of the imperium and functioning as the loca for communication with the divine, as well as the media between religion and its secular context, state ideology and local beliefs, or various ethnic groups. Through the discovery of a rich variety of historical sources, especially stele inscriptions preserved in the sacrificial temples, the contributors of the ten chapters in this volume examine the sacred peaks, strongholds, seas, and waterways respectively. While each of the chapters explores one or more perspectives, together they reveal the rich implications and ramification of the ritual system and present the first comprehensive study of this sub-field

    The Russian Empire, Slaving and Liberation, 1480–1725

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    The monograph realigns political culture and countermeasures against slave raids, which rose during the breakup of the Golden Horde. By physical defense of the open steppe border and embracing the New Israel symbolism (exodus from slavery in Egypt/among the Tatars), Muscovites found a defensive model to expand the empire. Recent debates on slaving are introduced to Russian and imperial history, while challenging entrenched perceptions of Muscovy

    Explainable Physics-informed Deep Learning for Rainfall-runoff Modeling and Uncertainty Assessment across the Continental United States

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    Hydrologic models provide a comprehensive tool to calibrate streamflow response to environmental variables. Various hydrologic modeling approaches, ranging from physically based to conceptual to entirely data-driven models, have been widely used for hydrologic simulation. During the recent years, however, Deep Learning (DL), a new generation of Machine Learning (ML), has transformed hydrologic simulation research to a new direction. DL methods have recently proposed for rainfall-runoff modeling that complement both distributed and conceptual hydrologic models, particularly in a catchment where data to support a process-based model is scared and limited. This dissertation investigated the applicability of two advanced probabilistic physics-informed DL algorithms, i.e., deep autoregressive network (DeepAR) and temporal fusion transformer (TFT), for daily rainfall-runoff modeling across the continental United States (CONUS). We benchmarked our proposed models against several physics-based hydrologic approaches such as the Sacramento Soil Moisture Accounting Model (SAC-SMA), Variable Infiltration Capacity (VIC), Framework for Understanding Structural Errors (FUSE), Hydrologiska ByrĂ„ns Vattenbalansavdelning (HBV), and the mesoscale hydrologic model (mHM). These benchmark models can be distinguished into two different groups. The first group are the models calibrated for each basin individually (e.g., SAC-SMA, VIC, FUSE2, mHM and HBV) while the second group, including our physics-informed approaches, is made up of the models that were regionally calibrated. Models in this group share one parameter set for all basins in the dataset. All the approaches were implemented and tested using Catchment Attributes and Meteorology for Large-sample Studies (CAMELS)\u27s Maurer datasets. We developed the TFT and DeepAR with two different configurations i.e., with (physics-informed model) and without (the original model) static attributes. Various catchment static and dynamic physical attributes were incorporated into the pipeline with various spatiotemporal variabilities to simulate how a drainage system responds to rainfall-runoff processes. To demonstrate how the model learned to differentiate between different rainfall–runoff behaviors across different catchments and to identify the dominant process, sensitivity and explainability analysis of modeling outcomes are also performed. Despite recent advancements, deep networks are perceived as being challenging to parameterize; thus, their simulation may propagate error and uncertainty in modeling. To address uncertainty, a quantile likelihood function was incorporated as the TFT loss function. The results suggest that the physics-informed TFT model was superior in predicting high and low flow fluctuations compared to the original TFT and DeepAR models (without static attributes) or even the physics-informed DeepAR. Physics-informed TFT model well recognized which static attributes more contributing to streamflow generation of each specific catchment considering its climate, topography, land cover, soil, and geological conditions. The interpretability and the ability of the physics-informed TFT model to assimilate the multisource of information and parameters make it a strong candidate for regional as well as continental-scale hydrologic simulations. It was noted that both physics-informed TFT and DeepAR were more successful in learning the intermediate flow and high flow regimes rather than the low flow regime. The advantage of the high flow can be attributed to learning a more generalizable mapping between static and dynamic attributes and runoff parameters. It seems both TFT and DeepAR may have enabled the learning of some true processes that are missing from both conceptual and physics-based models, possibly related to deep soil water storage (the layer where soil water is not sensitive to daily evapotranspiration), saturated hydraulic conductivity, and vegetation dynamics

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Inductive Bias in Machine Learning

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    Induktive Verzerrung beschreibt die PrĂ€ferenz fĂŒr Lösungen, welche ein Algorithmus fĂŒr maschinelles Lernen hat, bevor er Daten sieht. Sie ist notwendiger Bestandteil fĂŒr das Ziel des maschinellen Lernens, nĂ€mlich von einer Menge an Beispielen auf ungesehene Datenpunkte zu verallgemeinern. In der Praxis wird die induktive Verzerrung jedoch oft nicht explizit spezifiziert, was theoretisches VerstĂ€ndnis verhindert und das Vertrauen in maschinelles Lernen untergrĂ€bt. Am deutlichsten wird dieses Problem am zeitgenössischen Beispiel von deep learning, das zwar in vielen Anwendungen erfolgreich ist, aber auf einer Vielzahl schlecht verstandener Techniken und Heuristiken beruht. Ziel dieser Dissertation ist es, die versteckten induktiven Verzerrungen von Algorithmen des maschinellen Lernens aufzudecken. Im ersten Teil der Dissertation decken wir die induktive Verzerrung von NetGAN auf, einem komplexen generativen Graphenmodell, das scheinbar keine PrĂ€ferenzen hat. Wir stellen fest, dass die Ursache der Generalisierung nicht in der GAN-Architektur liegt, sondern in einer unscheinbaren Approximation mit niedrigem Rang. Wir nutzen diese Erkenntnis, um NetGAN von allen unnötigen Teilen, einschließlich des GAN, zu befreien und eine stark vereinfachte Reformulierung zu erhalten. Als NĂ€chstes prĂ€sentieren wir einen generischen Algorithmus, der die versteckte induktive Verzerrung in der approximativen Bayesschen Inferenz enthĂŒllt. WĂ€hrend die induktive Verzerrung bei der Bayesschen Inferenz vollstĂ€ndig durch den Prior beschrieben wird, greifen reale Anwendungen oft auf approximative Techniken zurĂŒck, die unkontrollierbare Fehler machen können. Indem wir das Problem in Form von inkompatiblen bedingten Verteilungen reformulieren, kommen wir zu einem generischen Algorithmus, der auf Pseudo-Gibbs-Sampling basiert und die Änderung der induktiven Verzerrung auf eine Änderung des Priors zurĂŒckfĂŒhrt. Der letzte Teil der Dissertation betrifft eine hĂ€ufige induktive Verzerrung beim kausalen Lernen, die Annahme unabhĂ€ngiger kausaler Mechanismen. Unter dieser Annahme betrachten wir SchĂ€tzer fĂŒr die StĂ€rke von Störfaktoren, die die Generalisierung von der Beobachtungsverteilung auf das zugrunde liegende kausale Modell bestimmt. Wir zeigen, dass ein bestehender SchĂ€tzer im Allgemeinen inkonsistent ist und prĂ€sentieren einen konsistenten SchĂ€tzer mit Werkzeugen aus der Theorie von Zufallsmatrizen.Inductive bias describes the preference for solutions that a machine learning algorithm holds before seeing any data. It is a necessary ingredient for the goal of machine learning, which is to generalize from a set of examples to unseen data points. Yet, the inductive bias of learning algorithms is often not specified explicitly in practice, which prevents a theoretical understanding and undermines trust in machine learning. This issue is most prominently visible in the contemporary case of deep learning, which is widely successful in applications but relies on many poorly understood techniques and heuristics. This thesis aims to uncover the hidden inductive biases of machine learning algorithms. In the first part of the thesis, we uncover the implicit inductive bias of NetGAN, a complex graph generative model with seemingly no prior preferences. We find that the root of its generalization properties does not lie in the GAN architecture but in an inconspicuous low-rank approximation. We then use this insight to strip NetGAN of all unnecessary parts, including the GAN, and obtain a highly simplified reformulation. Next, we present a generic algorithm that reverse-engineers hidden inductive bias in approximate Bayesian inference. While the inductive bias is completely described by the prior distribution in full Bayesian inference, real-world applications often resort to approximate techniques that can make uncontrollable errors. By reframing the problem in terms of incompatible conditional distributions, we arrive at a generic algorithm based on pseudo-Gibbs sampling that attributes the change in inductive bias to a change in the prior distribution. The last part of the thesis concerns a common inductive bias in causal learning, the assumption of independent causal mechanisms. Under this assumption, we consider estimators for confounding strength, which governs the generalization ability from observational distribution to the underlying causal model. We show that an existing estimator is generally inconsistent and propose a consistent estimator based on tools from random matrix theory

    Whom to blame for Judah’s doom?

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    The Babylonian exile is considered one of the worst trauma experiences in the context of HB/OT, with which a multitude of biblical texts are concerned. The final chapters of 2 Kings offer various theological interpretations of how and why the exile had to occur. Narratively, the study approaches the pericope and seeks to identify these interpretive models.Das babylonische Exil gilt als eine der schlimmsten Traumaerfahrungen im Kontext von HB/AT, mit der eine Vielzahl von biblischen Texten befasst ist. Der Schluss von 2 Könige bietet verschiedene theologische Deutungen an, wie und warum es zum Exil kommen musste. ErzÀhltechnisch nÀhert sich die Studie der ErzÀhlung an und sucht diese Deutungsmodelle zu identifizieren

    Mapping the Unmappable?

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    How can we map differing perceptions of the living environment? Mapping the Unmappable? explores the potential of cartography to communicate the relations of Africa's indigenous peoples with other human and non-human actors within their environments. These relations transcend Western dichotomies such as culture-nature, human-animal, natural-supernatural. The volume brings two strands of research - cartography and »relational« anthropology - into a closer dialogue. It provides case studies in Africa as well as lessons to be learned from other continents (e.g. North America, Asia and Australia). The contributors create a deepened understanding of indigenous ontologies for a further decolonization of maps, and thus advance current debates in the social sciences

    Operatic Pasticcios in 18th-Century Europe

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    In Early Modern times, techniques of assembling, compiling and arranging pre-existing material were part of the established working methods in many arts. In the world of 18th-century opera, such practices ensured that operas could become a commercial success because the substitution or compilation of arias fitting the singer's abilities proved the best recipe for fulfilling the expectations of audiences. Known as »pasticcios« since the 18th-century, these operas have long been considered inferior patchwork. The volume collects essays that reconsider the pasticcio, contextualize it, define its preconditions, look at its material aspects and uncover its aesthetical principles
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