1,192 research outputs found
The q-Analogue of the Extended Generalized Gamma Distribution
This project introduces a flexible univariate probability model referred to as the q-analogue of the Extended Generalized Gamma (or q-EGG) distribution, which encompasses the majority of the most frequently used continuous distributions, including the gamma, Weibull, logistic, type-1 and type-2 beta, Gaussian, Cauchy, Student-t and F. Closed form representations of its moments and cumulative distribution function are provided. Additionally, computational techniques are proposed for determining estimates of its parameters. Both the method of moments and the maximum likelihood approach are utilized. The effect of each parameter is also graphically illustrated. Certain data sets are modeled with q-EGG distributions; goodness of fit is assessed by making use of the Anderson–Darling and Cram´er–von Mises criteria, among others. Improved approximations to the distribution of quadratic forms are considered as well. Since much effort was expended to develop the code required to implement the various methodologi
Learning to Guide Decoding for Image Captioning
Recently, much advance has been made in image captioning, and an
encoder-decoder framework has achieved outstanding performance for this task.
In this paper, we propose an extension of the encoder-decoder framework by
adding a component called guiding network. The guiding network models the
attribute properties of input images, and its output is leveraged to compose
the input of the decoder at each time step. The guiding network can be plugged
into the current encoder-decoder framework and trained in an end-to-end manner.
Hence, the guiding vector can be adaptively learned according to the signal
from the decoder, making itself to embed information from both image and
language. Additionally, discriminative supervision can be employed to further
improve the quality of guidance. The advantages of our proposed approach are
verified by experiments carried out on the MS COCO dataset.Comment: AAAI-1
A globally convergent SQP-type method with least constraint violation for nonlinear semidefinite programming
We present a globally convergent SQP-type method with the least constraint
violation for nonlinear semidefinite programming. The proposed algorithm
employs a two-phase strategy coupled with a line search technique. In the first
phase, a subproblem based on a local model of infeasibility is formulated to
determine a corrective step. In the second phase, a search direction that moves
toward optimality is computed by minimizing a local model of the objective
function. Importantly, regardless of the feasibility of the original problem,
the iterative sequence generated by our proposed method converges to a
Fritz-John point of a transformed problem, wherein the constraint violation is
minimized. Numerical experiments have been conducted on various complex
scenarios to demonstrate the effectiveness of our approach.Comment: 34 page
Allocating Divisible Resources on Arms with Unknown and Random Rewards
We consider a decision maker allocating one unit of renewable and divisible
resource in each period on a number of arms. The arms have unknown and random
rewards whose means are proportional to the allocated resource and whose
variances are proportional to an order of the allocated resource. In
particular, if the decision maker allocates resource to arm in a
period, then the reward is, where
is the unknown mean and the noise is independent and
sub-Gaussian. When the order ranges from 0 to 1, the framework smoothly
bridges the standard stochastic multi-armed bandit and online learning with
full feedback. We design two algorithms that attain the optimal gap-dependent
and gap-independent regret bounds for , and demonstrate a phase
transition at . The theoretical results hinge on a novel concentration
inequality we have developed that bounds a linear combination of sub-Gaussian
random variables whose weights are fractional, adapted to the filtration, and
monotonic
Algorithmic Decision-Making Safeguarded by Human Knowledge
Commercial AI solutions provide analysts and managers with data-driven
business intelligence for a wide range of decisions, such as demand forecasting
and pricing. However, human analysts may have their own insights and
experiences about the decision-making that is at odds with the algorithmic
recommendation. In view of such a conflict, we provide a general analytical
framework to study the augmentation of algorithmic decisions with human
knowledge: the analyst uses the knowledge to set a guardrail by which the
algorithmic decision is clipped if the algorithmic output is out of bound, and
seems unreasonable. We study the conditions under which the augmentation is
beneficial relative to the raw algorithmic decision. We show that when the
algorithmic decision is asymptotically optimal with large data, the
non-data-driven human guardrail usually provides no benefit. However, we point
out three common pitfalls of the algorithmic decision: (1) lack of domain
knowledge, such as the market competition, (2) model misspecification, and (3)
data contamination. In these cases, even with sufficient data, the augmentation
from human knowledge can still improve the performance of the algorithmic
decision
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