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  1. In search of mechanisms: discoveries across the life sciences.Carl F. Craver - 2013 - London: University of Chicago Press. Edited by Lindley Darden.
    With In Search of Mechanisms, Carl F. Craver and Lindley Darden offer both a descriptive and an instructional account of how biologists discover mechanisms. Drawing on examples from across the life sciences and through the centuries, Craver and Darden compile an impressive toolbox of strategies that biologists have used and will use again to reveal the mechanisms that produce, underlie, or maintain the phenomena characteristic of living things. They discuss the questions that figure in the search for mechanisms, characterizing the (...)
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  • Explanation in artificial intelligence: Insights from the social sciences.Tim Miller - 2019 - Artificial Intelligence 267 (C):1-38.
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  • (2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–⁠32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
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  • Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
    Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...
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  • Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan - 2018 - Philosophy and Technology 32 (4):661-683.
    We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that (...)
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  • Clinical applications of machine learning algorithms: beyond the black box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  • Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - 2019 - Philosophy and Technology 34 (2):265-288.
    Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...)
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  • Deep learning: A philosophical introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10):e12625.
    Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation (...)
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  • (1 other version)The Pragmatic Turn in Explainable Artificial Intelligence (XAI).Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.
    In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...)
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  • Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...)
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  • Understanding Scientific Understanding.Henk W. de Regt - 2017 - New York: Oup Usa.
    Understanding is a central aim of science and highly important in present-day society. But what precisely is scientific understanding and how can it be achieved? This book answers these questions, through philosophical analysis and historical case studies, and presents a philosophical theory of scientific understanding that highlights its contextual nature.
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  • Scientific Explanation: Putting Communication First.Angela Potochnik - 2016 - Philosophy of Science 83 (5):721-732.
    Scientific explanations must bear the proper relationship to the world: they must depict what, out in the world, is responsible for the explanandum. But explanations must also bear the proper relationship to their audience: they must be able to create human understanding. With few exceptions, philosophical accounts of explanation either ignore entirely the relationship between explanations and their audience or else demote this consideration to an ancillary role. In contrast, I argue that considering an explanation’s communicative role is crucial to (...)
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  • Mind, Matter, and Metabolism.Peter Godfrey-Smith - 2016 - Journal of Philosophy 113 (10):481-506.
    I discuss the bearing on the mind-body problem of some general characteristics of living systems, including the physical basis of metabolism and the relation between living activity and cognitive capacities in simple organisms. I then attempt to describe stages in the history of animal life important to the evolution of subjective experience. Features of the biological basis of cognition are used to criticize arguments against materialism that draw on the conceivability of a separation between mental and physical. I also argue (...)
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  • The Scientific Image.William Demopoulos & Bas C. van Fraassen - 1982 - Philosophical Review 91 (4):603.
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  • Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research.William Bechtel & Robert C. Richardson - 2010 - Princeton.
    An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...)
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  • Understanding without explanation.Peter Lipton - 2008 - In Henk W. De Regt, Sabina Leonelli & Kai Eigner (eds.), Scientific Understanding: Philosophical Perspectives. University of Pittsburgh Press. pp. 43-63.
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  • The causal mechanical model of explanation.James Woodward - 1989 - Minnesota Studies in the Philosophy of Science 13:359-83.
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  • (1 other version)Four Decades of Scientific Explanation.Wesley C. Salmon & Anne Fagot-Largeault - 1989 - History and Philosophy of the Life Sciences 16 (2):355.
    As Aristotle stated, scientific explanation is based on deductive argument--yet, Wesley C. Salmon points out, not all deductive arguments are qualified explanations. The validity of the explanation must itself be examined. _Four Decades of Scientific Explanation_ provides a comprehensive account of the developments in scientific explanation that transpired in the last four decades of the twentieth century. It continues to stand as the most comprehensive treatment of the writings on the subject during these years. Building on the historic 1948 essay (...)
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  • Three kinds of new mechanism.Arnon Levy - 2013 - Biology and Philosophy 28 (1):99-114.
    I distinguish three theses associated with the new mechanistic philosophy – concerning causation, explanation and scientific methodology. Advocates of each thesis are identified and relationships among them are outlined. I then look at some recent work on natural selection and mechanisms. There, attention to different kinds of New Mechanism significantly affects of what is at stake.
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  • No understanding without explanation.Michael Strevens - 2013 - Studies in History and Philosophy of Science Part A 44 (3):510-515.
    Scientific understanding, this paper argues, can be analyzed entirely in terms of a mental act of “grasping” and a notion of explanation. To understand why a phenomenon occurs is to grasp a correct explanation of the phenomenon. To understand a scientific theory is to be able to construct, or at least to grasp, a range of potential explanations in which that theory accounts for other phenomena. There is no route to scientific understanding, then, that does not go by way of (...)
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  • Fuzzy Sets.Lofti A. Zadeh - 1965 - Information and Control 8 (1):338--53.
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  • Scientific Understanding: Philosophical Perspectives.Henk W. De Regt, Sabina Leonelli & Kai Eigner (eds.) - 2008 - University of Pittsburgh Press.
    The chapters in this book highlight the multifaceted nature of the process of scientific research.
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  • Aspects of Scientific Explanation and Other Essays in the Philosophy of Science.Carl Gustav Hempel - 1965 - New York: The Free Press.
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  • (1 other version)Scientific Explanation and the Causal Structure of the World.Wesley C. Salmon - 1984 - Princeton University Press.
    The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...)
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  • Depth: An Account of Scientific Explanation.Michael Strevens - 2008 - Cambridge: Harvard University Press.
    Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.
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  • Making things happen: a theory of causal explanation.James F. Woodward - 2003 - New York: Oxford University Press.
    Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.
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  • (1 other version)Explaining the brain: mechanisms and the mosaic unity of neuroscience.Carl F. Craver - 2007 - New York : Oxford University Press,: Oxford University Press, Clarendon Press.
    Carl Craver investigates what we are doing when we sue neuroscience to explain what's going on in the brain.
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  • The nature of explanation.Peter Achinstein - 1983 - New York: Oxford University Press.
    Offering a new approach to scientific explanation, this book focuses initially on the explaining act itself.
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  • Scientific explanation.James Woodward - 1979 - British Journal for the Philosophy of Science 30 (1):41-67.
    Issues concerning scientific explanation have been a focus of philosophical attention from Pre- Socratic times through the modern period. However, recent discussion really begins with the development of the Deductive-Nomological (DN) model. This model has had many advocates (including Popper 1935, 1959, Braithwaite 1953, Gardiner, 1959, Nagel 1961) but unquestionably the most detailed and influential statement is due to Carl Hempel (Hempel 1942, 1965, and Hempel & Oppenheim 1948). These papers and the reaction to them have structured subsequent discussion concerning (...)
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  • (1 other version)Studies in the logic of explanation.Carl Gustav Hempel & Paul Oppenheim - 1948 - Philosophy of Science 15 (2):135-175.
    To explain the phenomena in the world of our experience, to answer the question “why?” rather than only the question “what?”, is one of the foremost objectives of all rational inquiry; and especially, scientific research in its various branches strives to go beyond a mere description of its subject matter by providing an explanation of the phenomena it investigates. While there is rather general agreement about this chief objective of science, there exists considerable difference of opinion as to the function (...)
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  • Thinking about mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
    The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.
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  • Dermatologist-level classification of skin cancer with deep neural networks.Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun - 2017 - Nature 542 (7639):115-118.
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  • (2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2021 - Synthese 198 (10):9211-9242.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealisedexplanation gamein which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of (...)
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  • Understanding, Explanation, and Scientific Knowledge.Kareem Khalifa - 2017 - Cambridge, UK: Cambridge University Press.
    From antiquity to the end of the twentieth century, philosophical discussions of understanding remained undeveloped, guided by a 'received view' that takes understanding to be nothing more than knowledge of an explanation. More recently, however, this received view has been criticized, and bold new philosophical proposals about understanding have emerged in its place. In this book, Kareem Khalifa argues that the received view should be revised but not abandoned. In doing so, he clarifies and answers the most central questions in (...)
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  • (1 other version)Statistical explanation.Wesley C. Salmon - 1970 - In Robert G. Colodny (ed.), The Nature and Function of Scientific Theories: Essays in Contemporary Science and Philosophy. University of Pittsburgh Press. pp. 173--231.
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  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Cynthia Rudin - 2019 - Nature Machine Intelligence 1.
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  • Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning.Maya Krishnan - 2020 - Philosophy and Technology 33 (3):487-502.
    The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a “black box problem.” That is, machine learning algorithms are “opaque” to human users, failing to be “interpretable” or “explicable” in terms that would render categorization procedures “understandable.” The purpose of this paper is to challenge the widespread agreement about the existence (...)
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  • (1 other version)Studies in the Logic of Explanation.Carl Hempel & Paul Oppenheim - 1948 - Journal of Symbolic Logic 14 (2):133-133.
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  • A Contextual Approach to Scientific Understanding.Henk W. de Regt & Dennis Dieks - 2005 - Synthese 144 (1):137-170.
    Achieving understanding of nature is one of the aims of science. In this paper we offer an analysis of the nature of scientific understanding that accords with actual scientific practice and accommodates the historical diversity of conceptions of understanding. Its core idea is a general criterion for the intelligibility of scientific theories that is essentially contextual: which theories conform to this criterion depends on contextual factors, and can change in the course of time. Our analysis provides a general account of (...)
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  • Mechanism and Biological Explanation.William Bechtel - 2011 - Philosophy of Science 78 (4):533-557.
    This article argues that the basic account of mechanism and mechanistic explanation, involving sequential execution of qualitatively characterized operations, is itself insufficient to explain biological phenomena such as the capacity of living organisms to maintain themselves as systems distinct from their environment. This capacity depends on cyclic organization, including positive and negative feedback loops, which can generate complex dynamics. Understanding cyclically organized mechanisms with complex dynamics requires coordinating research directed at decomposing mechanisms into parts and operations with research using computational (...)
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  • (2 other versions)The Explanation Game: A Formal Framework for Interpretable Machine Learning.David S. Watson & Luciano Floridi - 2021 - In Josh Cowls & Jessica Morley (eds.), The 2020 Yearbook of the Digital Ethics Lab. Springer Verlag. pp. 109-143.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
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  • Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board.S. P. Somashekhar, M. -J. Sepúlveda, S. Puglielli, A. D. Norden, E. H. Shortliffe, C. Rohit Kumar, A. Rauthan, N. Arun Kumar, P. Patil, K. Rhee & Y. Ramya - 2018 - Annals of Oncology 29 (2):418-423.
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  • Mechanistic Explanation of Biological Processes.Derek John Skillings - 2015 - Philosophy of Science 82 (5):1139-1151.
    Biological processes are often explained by identifying the underlying mechanisms that generate a phenomenon of interest. I characterize a basic account of mechanistic explanation and then present three challenges to this account, illustrated with examples from molecular biology. The basic mechanistic account is insufficient for explaining nonsequential and nonlinear dynamic processes, is insufficient for explaining the inherently stochastic nature of many biological mechanisms, and fails to give a proper framework for analyzing organization. I suggest that biological processes are best approached (...)
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  • (1 other version)The Pragmatic Turn in Explainable Artificial Intelligence.Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.
    In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...)
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  • Artificial Intelligence and Black‐Box Medical Decisions: Accuracy versus Explainability.Alex John London - 2019 - Hastings Center Report 49 (1):15-21.
    Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...)
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  • Focusing on scientific understanding.Henk W. de Regt, Sabina Leonelli & K. Eigner - 2008 - In Henk W. De Regt, Sabina Leonelli & Kai Eigner (eds.), Scientific Understanding: Philosophical Perspectives. University of Pittsburgh Press.
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  • Discovering Cell Mechanisms: The Creation of Modern Cell Biology.William Bechtel - 2005 - Cambridge University Press.
    Between 1940 and 1970 pioneers in the new field of cell biology discovered the operative parts of cells and their contributions to cell life. They offered mechanistic accounts that explained cellular phenomena by identifying the relevant parts of cells, the biochemical operations they performed, and the way in which these parts and operations were organised to accomplish important functions. Cell biology was a revolutionary science but in this book it also provides fuel for yet another revolution, one that focuses on (...)
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  • Discovering Cell Mechanisms: The Creation of Modern Cell Biology.William Bechtel - 2007 - Journal of the History of Biology 40 (1):185-187.
    Between 1940 and 1970 pioneers in the new field of cell biology discovered the operative parts of cells and their contributions to cell life. They offered mechanistic accounts that explained cellular phenomena by identifying the relevant parts of cells, the biochemical operations they performed, and the way in which these parts and operations were organised to accomplish important functions. Cell biology was a revolutionary science but in this book it also provides fuel for yet another revolution, one that focuses on (...)
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  • Distilling a Neural Network Into a Soft Decision Tree.Nicholas Frosst & Geoffrey Hinton - 2017 - .
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