Applications of advances in nonlinear sensitivity analysis

Werbos, Paul J.

doi:10.1007/bfb0006203

Cited by 202 publications

(148 citation statements)

References 0 publications

Supporting

Mentioning

145

Contrasting

Unclassified

Order By: Relevance

“…To my knowledge, the first NN-specific application of efficient BP as above was described in 1981 (Werbos, 1981(Werbos, , 2006. Related work was published several years later (Parker, 1985;LeCun, 1985LeCun, , 1988.…”

Section: -1981 and Beyond: Development Of Backpropagation (Bp) Fomentioning

confidence: 99%

“…In the special case of an RL FNN controller C interacting with a deterministic, predictable environment, a separate FNN called M can learn to become C's world model through system identification, predicting C's inputs from previous actions and inputs (e.g., Werbos, 1981Werbos, , 1987Munro, 1987;Jordan, 1988;Werbos, 1989b,a;Robinson and Fallside, 1989;Jordan and Rumelhart, 1990;Schmidhuber, 1990d;Narendra and Parthasarathy, 1990;Werbos, 1992;Gomi and Kawato, 1993;Cochocki and Unbehauen, 1993;Levin and Narendra, 1995;Miller et al, 1995;Ljung, 1998;Prokhorov et al, 2001;Ge et al, 2010). Assume M has learned to produce accurate predictions.…”

Section: Rl Through Nn World Models Yields Rnns With Deep Capsmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning in neural networks: An overview

2015

View full text Add to dashboard Cite

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.LATEX source: http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.tex Complete BIBTEX file (888 kB): http://www.idsia.ch/˜juergen/deep.bib Preface This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using "local search" to follow citations of citations backwards in time. Since not all DL publications properly acknowledge earlier relevant work, additional global search strategies were employed, aided by consulting numerous neural network experts. As a result, the present preprint mostly consists of references. Nevertheless, through an expert selection bias I may have missed important work. A related bias was surely introduced by my special familiarity with the work of my own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to juergen@idsia.ch.

show abstract

Section: -1981 and Beyond: Development Of Backpropagation (Bp) Fomentioning

confidence: 99%

Section: Rl Through Nn World Models Yields Rnns With Deep Capsmentioning

confidence: 99%

Deep learning in neural networks: An overview

2015

View full text Add to dashboard Cite

show abstract

“…, n 1 . This is done using the back-propagation algorithm, which in today's form goes back to Werbos (1982).…”

Section: Categorical Case (With More Than Two Categorical Classes)mentioning

confidence: 99%

An Individual Claims History Simulation Machine

Gabrielli

Wüthrich

2018

Risks

View full text Add to dashboard Cite

Abstract:The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.

show abstract

“…Алгоритм был предложен в 1970 году в магистерской диссертации [34,35] без связи с нейронными сетями. Первое применение этого алгоритма для обучения нейронных сетей описано в работе [36], вышедшей в 1981 году. После этого появилось еще несколько работ на эту тему [37][38][39].…”

Section: обучение нейронных сетейunclassified

An Overview of Methods for Deep Learning in Neural Networks

2017

CMSE

View full text Add to dashboard Cite

Applications of advances in nonlinear sensitivity analysis

Cited by 202 publications

References 0 publications

Deep learning in neural networks: An overview

Deep learning in neural networks: An overview

An Individual Claims History Simulation Machine

An Overview of Methods for Deep Learning in Neural Networks

Contact Info

Product

Resources

About