TY - JOUR
TI - On Emergence and Explanation
AU - Baas, Nils Andreas
AU - Emmeche, Claus
T2 - Intellectica. Revue de l'Association pour la Recherche Cognitive
AB - Emergence is a universal phenomenon that can be defined mathematically in a very general way. This is useful for the study of scientifically legitimate explanations of complex systems, here defined as hyperstructures. A requirement is that observation mechanisms are considered within the general framework. Two notions of emergence are defined, and specific examples of these are discussed.
DA - 1997///
PY - 1997
DO - 10/ggdf9z
DP - Crossref
VL - 25
IS - 2
SP - 67
EP - 83
LA - en
SN - 0769-4113
UR - https://www.persee.fr/doc/intel_0769-4113_1997_num_25_2_1558
Y2 - 2019/11/22/17:59:11
KW - Biology
KW - Emergence
ER -
TY - JOUR
TI - A Tutorial on Learning With Bayesian Networks
AU - Heckerman, David
AB - 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 …
DA - 1995/03/01/
PY - 1995
DP - www.microsoft.com
LA - en-US
UR - https://www.microsoft.com/en-us/research/publication/a-tutorial-on-learning-with-bayesian-networks/
Y2 - 2019/11/22/19:09:15
KW - Bayesianism
KW - Classical ML
KW - Machine learning
ER -
TY - JOUR
TI - On the Computational Power of Neural Nets
AU - Siegelmann, H. T.
AU - Sontag, E. D.
T2 - Journal of Computer and System Sciences
AB - 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.
DA - 1995/02/01/
PY - 1995
DO - 10/dvwtc3
DP - ScienceDirect
VL - 50
IS - 1
SP - 132
EP - 150
J2 - Journal of Computer and System Sciences
LA - en
SN - 0022-0000
UR - http://www.sciencedirect.com/science/article/pii/S0022000085710136
Y2 - 2019/11/28/17:50:06
KW - Classical ML
KW - Machine learning
ER -
TY - JOUR
TI - Probabilistic Non-determinism
AU - Jones, Claire
DA - 1989///
PY - 1989
DP - Zotero
SP - 198
LA - en
KW - Denotational semantics
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - Linear logic
AU - Girard, Jean-Yves
T2 - Theoretical Computer Science
AB - The familiar connective of negation is broken into two operations: linear negation which is the purely negative part of negation and the modality “of course” which has the meaning of a reaffirmation. Following this basic discovery, a completely new approach to the whole area between constructive logics and programmation is initiated.
DA - 1987/01/01/
PY - 1987
DO - 10/cmv5mj
DP - ScienceDirect
VL - 50
IS - 1
SP - 1
EP - 101
J2 - Theoretical Computer Science
LA - en
SN - 0304-3975
UR - http://www.sciencedirect.com/science/article/pii/0304397587900454
Y2 - 2019/11/26/21:07:06
KW - Denotational semantics
KW - Linear logic
KW - Type theory
ER -
TY - JOUR
TI - LCF considered as a programming language
AU - Plotkin, G. D.
T2 - Theoretical Computer Science
AB - The paper studies connections between denotational and operational semantics for a simple programming language based on LCF. It begins with the connection between the behaviour of a program and its denotation. It turns out that a program denotes ⊥ in any of several possible semantics if it does not terminate. From this it follows that if two terms have the same denotation in one of these semantics, they have the same behaviour in all contexts. The converse fails for all the semantics. If, however, the language is extended to allow certain parallel facilities behavioural equivalence does coincide with denotational equivalence in one of the semantics considered, which may therefore be called “fully abstract”. Next a connection is given which actually determines the semantics up to isomorphism from the behaviour alone. Conversely, by allowing further parallel facilities, every r.e. element of the fully abstract semantics becomes definable, thus characterising the programming language, up to interdefinability, from the set of r.e. elements of the domains of the semantics.
DA - 1977/12/01/
PY - 1977
DO - 10/dc7fdn
DP - ScienceDirect
VL - 5
IS - 3
SP - 223
EP - 255
J2 - Theoretical Computer Science
LA - en
SN - 0304-3975
UR - http://www.sciencedirect.com/science/article/pii/0304397577900445
Y2 - 2019/11/26/16:59:50
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - The representation of biological systems from the standpoint of the theory of categories
AU - Rosen, Robert
T2 - The Bulletin of Mathematical Biophysics
DA - 1958/12//
PY - 1958
DO - 10/fdgzxz
DP - Crossref
VL - 20
IS - 4
SP - 317
EP - 341
LA - en
SN - 0007-4985, 1522-9602
UR - http://link.springer.com/10.1007/BF02477890
Y2 - 2019/11/22/18:55:09
KW - Biology
KW - Sketchy
ER -