Epstein Secrets

our discussion of semiotic learning according to Charles Sanders Peirce's theory of semiosis [Pei341, in w

here is a trick to over-coming this that is deeply embedded in the theory of neural networks, and yet completely ignored in probabilistic (Bayesian, Markovian) networks: the sig- moid function. The sigmoid function serves to focus in on a single stimulus, and elevate its importance, and, at the same time,

ctors. By classifying a large number of words as 'modifiers of nouns', one is essentially admitting that they are equi-probable in that role, in the Markovian senselAsh651 (equivalently, treating them as equally-weighted priors, in the Bayesian probability sense). That is, given the word "car", we should

FROM 1* DESK OF CHARLES L. HARPER JR. Jeffrey Epstein (I13 pages) (Via

quantum speedup in photosynthetic light harvesting. Complete characterization of mixing time for the continuous quantum walk on the hypercube with Markovian decoherence Optimal quantum multiparameter estimation as applied to dipole- and exchange-coupled qubits. Quantum non-demolition measurements of si

FROM 1* DESK OF CHARLES L. HARPER JR. Jeffrey Epstein (IS pages) (Via

quantum speedup in photosynthetic light harvesting. Complete characterization of mixing time for the continuous quantum walk on the hypercube with Markovian decoherence Optimal quantum multiparameter estimation as applied to dipole- and exchange-coupled qubits. Quantum non-demolition measurements of si