14,987 research outputs found
Optimal Entry and Consumption under Habit Formation
This paper studies a composite problem involving the decision making of the
optimal entry time and dynamic consumption afterwards. In stage-1, the investor
has access to full market information subjecting to some information costs and
needs to choose an optimal stopping time to initiate stage-2; in stage-2, the
investor terminates the costly full information acquisition and starts dynamic
investment and consumption under partial observations of free public stock
prices. The habit formation preference is employed, in which the past
consumption affects the investor's current decisions. By using the stochastic
Perron's method, the value function of the composite problem is proved to be
the unique viscosity solution of some variational inequalities.Comment: Final version, forthcoming in Advances in Applied Probabilit
Sketch-a-Net that Beats Humans
We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch rather than natural photo statistics, (ii) a multi-channel
generalisation that encodes sequential ordering in the sketching process, and
(iii) a multi-scale network ensemble with joint Bayesian fusion that accounts
for the different levels of abstraction exhibited in free-hand sketches. We
show that state-of-the-art deep networks specifically engineered for photos of
natural objects fail to perform well on sketch recognition, regardless whether
they are trained using photo or sketch. Our network on the other hand not only
delivers the best performance on the largest human sketch dataset to date, but
also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral
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