The politics of learning from rare events
Webb18 feb. 2024 · Here we examine the politics of learning in a historical analysis of population-level learning by four different actors following the 1994 sinking of the ferry … Webb1 okt. 2009 · Our analysis of these rare events, grounded in data collected through interviews and archival materials, reveals that the issue is not so much what organizations learn "from" rare events but what ...
The politics of learning from rare events
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Webb1 jan. 2013 · The analytical and critical device used is the idea of the ‘politics of learning’, through which the author highlights the powerful work that is being done by, and which … WebbThe literature on policy learning has generated a huge amount of heat (and some light) producing policy learning taxonomies, concepts and methods, yet the efforts to …
Webb18 feb. 2024 · Existing models of learning from rare events have elided conflict and politics by assuming that individuals and organizations always seek new valid … Webbrare morphological variants (Arthur et al.,2016; Koehn and Knowles,2024;Nguyen and Chiang, 2024). Learning from rare events is difficult due to the fact that the model parameters are not ade-quately updated. For example, the embeddings of the rare words are only updated a few times during training, and similarly for the patterns learned by
Webb1 maj 2024 · Dealing with “rare” events in Machine Learning If you are in Data Science, sooner or later, you will have to deal with a common problem — “rare” events! If the rate of occurrence of the... WebbHere we examine the politics of learning in a historical analysis of population-level learning by four different actors following the 1994 sinking of the ferry Estonia. We show how …
Webb12 apr. 2024 · The 10th HEC-McGill Winter Finance Workshop took place from March 26-28, 2024, in Ischgl, Austria, in collaboration with the Faculty of Law, Economics and Finance (FDEF) at the University of Luxembourg. This year’s conference, once again sponsored by the Desmarais Global Finance Research Centre (DGFRC), brought together academics …
Webb1 feb. 2024 · Learning from rare events. Fault Tree Analysis. Reliability Block Diagrams. ValuJet Flight 592. Swissair Flight SR 111. 1. Introduction. Common sense suggests that failure is something bad and must be avoided, as failure challenge organisations, their people, functions and processes unusual. how many episodes does aldnoah zero haveWebb19 juli 2024 · As we go along, all ten tips will be archived at this machine learning best practices page. Machine learning commonly requires the use of highly unbalanced data. When detecting fraud or isolating manufacturing defects, for example, the target event is extremely rare – often way below 1 percent. So, even if you’re using a model that’s 99 ... high value investment firmsWebb26 apr. 2024 · 1 Introduction. We consider a particular class of reinforcement learning problems where only rare events can result in non-zero rewards and when the agent can … how many episodes does 24 have as of 2022WebbDownloadable! How do political actors learn about their environment when the “data†provided by political processes are characterized by rare events and highly discontinuous variation? In such learning environments, what can theory predict about how learning actors will take costly actions that are difficult to reverse (e.g., eliminating programs, … high value item sheet army pubsWebb9 mars 2024 · Learning to Remember Rare Events. Łukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio. Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. how many episodes does a mini series haveWebbAll learning has uncertain consequences, but learning from rare events is especially problema ... "Understanding the Challenger disaster: Organizational structure and the … how many episodes does anna kdrama haveWebb15 jan. 2024 · Rare events represent a great analytical challenge. The maximum likelihood-based (ML) binary logit model as the workhorse model in the social sciences can generate heavily biased parameter estimates if events are rare. In detail, the finite sample bias in ML estimates may be substantially larger than that observed in cases with balanced data of ... high value item sheet 4986