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Table 3 shows results for the generalization of explana- tions from contexts to situations discussed in [20]. Without going into the details here, in this scenario the epistemic state consists of a set a201 of pairs a16a57a61
2002
"... In PAGE 4: ... The problem Explanation Existence is associated with the important task of finding an explanation for an event a110 . Similar as in other frameworks for explanations Table 1: Complexity of Explanations Problem general case binary case Explanation a184a35a185a186 -complete a184a35a185 -complete Explanation Existence a187 a185 a188 -complete a187 a185 a186 -complete a189 -Partial Explanation a190a42a191 a168 a104 a192 -complete a190a88a193 a185 a192 -complete a189 -Partial Explanation Existence a187 a185 a188 -complete a187 a185 a186 -complete Partial Explanation a190 a191 a168 a104 a192 -complete a190 a193 a185 a192 -complete Explanatory Power a194a72a190a42a191 a168 a104 a192 -complete a194a72a190 a193 a185 a192 -complete Table 2: Complexity of Explanations: Succinct Contexts Problem general case binary case Explanation a195 a185 a196 -complete a195 a185 a188 -complete Partial Explanation a195 a185 a196 -complete a195 a185 a188 -complete Table3 : Complexity of Explanations: Situations Problem general case binary case Explanation a184 a185 a186 -complete a184 a185 -complete (e.g.... ..."
Cited by 7
Table 2 presents a comparison between difierent proposals in the literature with regard to the kinds of uniqueness, alias encapsulation, and borrowing they pro- vide. For reasons of space, we restrict our discussion mainly to object-oriented programming languages. For a few pointers into less closely related literature, see e.g., [11, 13], or for a recent discussion on aliasing in general, see [16].
2003
"... In PAGE 18: ... Table2 . Comparison.... In PAGE 20: ... Guava [3] also uses lent parameters to avoid capturing of objects. Some remarks regarding Table2 follow. (a) PRFJ [9] permits object graphs which violates deep ownership, but it uses an efiects system to prevent access through the ofiending references.... ..."
Cited by 52
Table 1: Order of magnitude of space and time requirements for the various general-purpose algorithms discussed here. Here c denotes a constant and the meaning of all the other symbols used is summarized in Section 3.3. Note: For the variant of BPTT(h) in which past weight values are saved, the space requirements are in (wAh).
1995
Cited by 84
Table 1: Order of magnitude of space and time requirements for the various general-purpose algorithms discussed here. Here c denotes a constant and the meaning of all the other symbols used is summarized in Section 3.3. Note: For the variant of BPTT(h) in which past weight values are saved, the space requirements are in (wAh).
"... In PAGE 17: ... This amounts to an average of (nT wA) operations per time step. Thus the space complexity of the gradient computation for RTRL is in (nwA), and its average time complexity per time step is in (wUwA), as indicated in Table1 . When the network is fully connected and all weights are adaptable, this algorithm has space complexity in (n3) and average time complexity per time step in (n4), as shown in Table 2.... In PAGE 32: ...12 Note that BPTT(16;8) is thus well over 50 times faster than RTRL on this task. |||||||||||||||||||||||| Insert Table1 about here. |||||||||||||||||||||||| |||||||||||||||||||||||| Insert Table 2 about here.... ..."
Table 1. Dictionary generalizations.
2002
"... In PAGE 2: ... The aim was to extend the coverage of the original set, while at the same time trying to mini- mize any decrease in accuracy. Table1 shows some examples of name generalizations. We used this generalized dictionary-based tagger for supplying a pre-tagged input to some of the learning methods that will be discussed in the following sec- tions.... ..."
Cited by 7
Table 1.1 A summary of the results discussed in this paper. Column 2 contains previously known results for general graphs. Column 3 gives bounds on bipartite networks based on the improved bound on L. Column 4 gives our new results based on the two-edge push rule.
TABLE 1.1 A summary of the results discussed in this paper. Column 2 contains previously known results for general graphs. Column 3 gives bounds on bipartite networks based on the improved bound on L. Column 4 gives our new results based on the two-edge push rule.
Table 1. Derivatives and their inverses: reconstructions by integration. D is the dimension. p is the characteristic (also non-integer) power of frequency in the Derivative Transfer Function. Note that for p=0 the Generalized Vector Gradient and its Inverse are equal to the Vector Hilbert Transform and its Inverse as discussed in [4].
2001
"... In PAGE 4: ...ccording to the chain (cf. fig. 1c) {Derivative operator, Non-linear Point Processing, Inverse Derivative operator} a variety of enhancement methods can be constructed. Table1 gives a survey of the derivatives to be chosen. The integrations are up to zero-derivative terms.... ..."
Cited by 3
Table 1: Generalized Lyapunov equations
2000
"... In PAGE 20: ...82 remain valid if the matrix G is positive de nite only on the subspace im Pr. In Table1 we review the generalized continuous-time and discrete-time Lyapunov equa- tions with di erent right-hand sides discussed in this section. 3 Inertia theorems The constrained generalized Lyapunov equations can be used to generalize some inertia theorems for matrices, e.... ..."
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