@article{heckerman_tutorial_1995,
title = {A {Tutorial} on {Learning} {With} {Bayesian} {Networks}},
url = {https://www.microsoft.com/en-us/research/publication/a-tutorial-on-learning-with-bayesian-networks/},
abstract = {A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can …},
language = {en-US},
urldate = {2019-11-22},
author = {Heckerman, David},
month = mar,
year = {1995},
note = {ZSCC: 0000058},
keywords = {Bayesianism, Classical ML, Machine learning}
}
@article{siegelmann_computational_1995,
title = {On the {Computational} {Power} of {Neural} {Nets}},
volume = {50},
issn = {0022-0000},
url = {http://www.sciencedirect.com/science/article/pii/S0022000085710136},
doi = {10/dvwtc3},
abstract = {This paper deals with finite size networks which consist of interconnections of synchronously evolving processors. Each processor updates its state by applying a "sigmoidal" function to a linear combination of the previous states of all units. We prove that one may simulate all Turing machines by such nets. In particular, one can simulate any multi-stack Turing machine in real time, and there is a net made up of 886 processors which computes a universal partial-recursive function. Products (high order nets) are not required, contrary to what had been stated in the literature. Non-deterministic Turing machines can be simulated by non-deterministic rational nets, also in real time. The simulation result has many consequences regarding the decidability, or more generally the complexity, of questions about recursive nets.},
language = {en},
number = {1},
urldate = {2019-11-28},
journal = {Journal of Computer and System Sciences},
author = {Siegelmann, H. T. and Sontag, E. D.},
month = feb,
year = {1995},
note = {ZSCC: 0000002},
keywords = {Classical ML, Machine learning},
pages = {132--150}
}
@book{engeler_combinatory_1995,
series = {Progress in {Theoretical} {Computer} {Science}},
title = {The {Combinatory} {Programme}},
isbn = {978-0-8176-3801-6},
url = {https://www.springer.com/gb/book/9780817638016},
abstract = {Combinatory logic started as a programme in the foundation of mathematics and in an historical context at a time when such endeavours attracted the most gifted among the mathematicians. This small volume arose under quite differ ent circumstances, namely within the context of reworking the mathematical foundations of computer science. I have been very lucky in finding gifted students who agreed to work with me and chose, for their Ph. D. theses, subjects that arose from my own attempts 1 to create a coherent mathematical view of these foundations. The result of this collaborative work is presented here in the hope that it does justice to the individual contributor and that the reader has a chance of judging the work as a whole. E. Engeler ETH Zurich, April 1994 lCollected in Chapter III, An Algebraization of Algorithmics, in Algorithmic Properties of Structures, Selected Papers of Erwin Engeler, World Scientific PubJ. Co. , Singapore, 1993, pp. 183-257. I Historical and Philosophical Background Erwin Engeler In the fall of 1928 a young American turned up at the Mathematical Institute of Gottingen, a mecca of mathematicians at the time; he was a young man with a dream and his name was H. B. Curry. He felt that he had the tools in hand with which to solve the problem of foundations of mathematics mice and for all. His was an approach that came to be called "formalist" and embodied that later became known as Combinatory Logic.},
language = {en},
urldate = {2019-11-26},
publisher = {Birkhäuser Basel},
author = {Engeler, Erwin},
year = {1995},
doi = {10.1007/978-1-4612-4268-0},
note = {ZSCC: NoCitationData[s1] },
keywords = {Algebra, Programming language theory, Purely theoretical}
}