792 research outputs found
A full-discrete exponential Euler approximation of invariant measure for parabolic stochastic partial differential equations
We discrete the ergodic semilinear stochastic partial differential equations
in space dimension with additive noise, spatially by a spectral
Galerkin method and temporally by an exponential Euler scheme. It is shown that
both the spatial semi-discretization and the spatio-temporal full
discretization are ergodic. Further, convergence orders of the numerical
invariant measures, depending on the regularity of noise, are recovered based
on an easy time-independent weak error analysis without relying on Malliavin
calculus. To be precise, the convergence order is in space and
in time for the space-time white noise case and
in space and in time for the trace class noise case
in space dimension , with arbitrarily small . Numerical
results are finally reported to confirm these theoretical findings.Comment: 27 pages, to appear in: Applied Numerical Mathematic
Item Response Theory based Ensemble in Machine Learning
In this article, we propose a novel probabilistic framework to improve the
accuracy of a weighted majority voting algorithm. In order to assign higher
weights to the classifiers which can correctly classify hard-to-classify
instances, we introduce the Item Response Theory (IRT) framework to evaluate
the samples' difficulty and classifiers' ability simultaneously. Three models
are created with different assumptions suitable for different cases. When
making an inference, we keep a balance between the accuracy and complexity. In
our experiment, all the base models are constructed by single trees via
bootstrap. To explain the models, we illustrate how the IRT ensemble model
constructs the classifying boundary. We also compare their performance with
other widely used methods and show that our model performs well on 19 datasets
The Bayesian Inversion Problem for Thermal Average Sampling of Quantum Systems
In this article, we propose a novel method for sampling potential functions
based on noisy observation data of a finite number of observables in quantum
canonical ensembles, which leads to the accurate sampling of a wide class of
test observables. The method is based on the Bayesian inversion framework,
which provides a platform for analyzing the posterior distribution and
naturally leads to an efficient numerical sampling algorithm. We highlight
that, the stability estimate is obtained by treating the potential functions as
intermediate variables in the following way: the discrepancy between two sets
of observation data of training observables can bound the distance between
corresponding posterior distributions of potential functions, while the latter
naturally leads to a bound of the discrepancies between corresponding thermal
averages of test observables. Besides, the training observables can be more
flexible than finite samples of the local density function, which are mostly
used in previous researches. The method also applies to the multi-level quantum
systems in the non-adiabatic regime. In addition, we provide extensive
numerical tests to verify the accuracy and efficiency of the proposed
algorithm
Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation
In this work, we demonstrate yet another approach to tackle the amodal
segmentation problem. Specifically, we first introduce a new representation,
namely a semantics-aware distance map (sem-dist map), to serve as our target
for amodal segmentation instead of the commonly used masks and heatmaps. The
sem-dist map is a kind of level-set representation, of which the different
regions of an object are placed into different levels on the map according to
their visibility. It is a natural extension of masks and heatmaps, where modal,
amodal segmentation, as well as depth order information, are all
well-described. Then we also introduce a novel convolutional neural network
(CNN) architecture, which we refer to as semantic layering network, to estimate
sem-dist maps layer by layer, from the global-level to the instance-level, for
all objects in an image. Extensive experiments on the COCOA and D2SA datasets
have demonstrated that our framework can predict amodal segmentation, occlusion
and depth order with state-of-the-art performance.Comment: This paper is submitted to ACMMM1
Mean-square approximations of L\'{e}vy noise driven SDEs with super-linearly growing diffusion and jump coefficients
This paper first establishes a fundamental mean-square convergence theorem
for general one-step numerical approximations of L\'{e}vy noise driven
stochastic differential equations with non-globally Lipschitz coefficients.
Then two novel explicit schemes are designed and their convergence rates are
exactly identified via the fundamental theorem. Different from existing works,
we do not impose a globally Lipschitz condition on the jump coefficient but
formulate appropriate assumptions to allow for its super-linear growth.
However, we require that the L\'{e}vy measure is finite. New arguments are
developed to handle essential difficulties in the convergence analysis, caused
by the super-linear growth of the jump coefficient and the fact that higher
moment bounds of the Poisson increments \int_t^{t+h} \int_Z
\,\bar{N}(\mbox{d}s,\mbox{d}z), t \geq 0, h >0 contribute to magnitude not
more than . Numerical results are finally reported to confirm the
theoretical findings.Comment: 34pages, 2 figure
Large deviations principles of sample paths and invariant measures of numerical methods for parabolic SPDEs
For parabolic stochastic partial differential equations (SPDEs), we show that
the numerical methods, including the spatial spectral Galerkin method and
further the full discretization via the temporal accelerated exponential Euler
method, satisfy the uniform sample path large deviations. Combining the
exponential tail estimate of invariant measures, we establish the large
deviations principles (LDPs) of invariant measures of these numerical methods.
Based on the error estimate between the rate function of the considered
numerical methods and that of the original equation, we prove that these
numerical methods can weakly asymptotically preserve the LDPs of sample paths
and invariant measures of the original equation. This work provides an approach
to proving the weakly asymptotical preservation for the above two LDPs for
SPDEs with small noise via numerical methods, by means of the minimization
sequences
Learning to Optimize Tensor Programs
We introduce a learning-based framework to optimize tensor programs for deep
learning workloads. Efficient implementations of tensor operators, such as
matrix multiplication and high dimensional convolution, are key enablers of
effective deep learning systems. However, existing systems rely on manually
optimized libraries such as cuDNN where only a narrow range of server class
GPUs are well-supported. The reliance on hardware-specific operator libraries
limits the applicability of high-level graph optimizations and incurs
significant engineering costs when deploying to new hardware targets. We use
learning to remove this engineering burden. We learn domain-specific
statistical cost models to guide the search of tensor operator implementations
over billions of possible program variants. We further accelerate the search by
effective model transfer across workloads. Experimental results show that our
framework delivers performance competitive with state-of-the-art hand-tuned
libraries for low-power CPU, mobile GPU, and server-class GPU.Comment: NeurIPS 201
Costly Features Classification using Monte Carlo Tree Search
We consider the problem of costly feature classification, where we
sequentially select the subset of features to make a balance between the
classification error and the feature cost. In this paper, we first cast the
task into a MDP problem and use Advantage Actor Critic algorithm to solve it.
In order to further improve the agent's performance and make the policy
explainable, we employ the Monte Carlo Tree Search to update the policy
iteratively. During the procedure, we also consider its performance on the
unbalanced dataset and its sensitivity to the missing value. We evaluate our
model on multiple datasets and find it outperforms other methods
Photo-Realistic Facial Details Synthesis from Single Image
We present a single-image 3D face synthesis technique that can handle
challenging facial expressions while recovering fine geometric details. Our
technique employs expression analysis for proxy face geometry generation and
combines supervised and unsupervised learning for facial detail synthesis. On
proxy generation, we conduct emotion prediction to determine a new
expression-informed proxy. On detail synthesis, we present a Deep Facial Detail
Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs
both geometry and appearance loss functions. For geometry, we capture 366
high-quality 3D scans from 122 different subjects under 3 facial expressions.
For appearance, we use additional 20K in-the-wild face images and apply
image-based rendering to accommodate lighting variations. Comprehensive
experiments demonstrate that our framework can produce high-quality 3D faces
with realistic details under challenging facial expressions
Relay: A High-Level Compiler for Deep Learning
Frameworks for writing, compiling, and optimizing deep learning (DL) models
have recently enabled progress in areas like computer vision and natural
language processing. Extending these frameworks to accommodate the rapidly
diversifying landscape of DL models and hardware platforms presents challenging
tradeoffs between expressivity, composability, and portability. We present
Relay, a new compiler framework for DL. Relay's functional, statically typed
intermediate representation (IR) unifies and generalizes existing DL IRs to
express state-of-the-art models. The introduction of Relay's expressive IR
requires careful design of domain-specific optimizations, addressed via Relay's
extension mechanisms. Using these extension mechanisms, Relay supports a
unified compiler that can target a variety of hardware platforms. Our
evaluation demonstrates Relay's competitive performance for a broad class of
models and devices (CPUs, GPUs, and emerging accelerators). Relay's design
demonstrates how a unified IR can provide expressivity, composability, and
portability without compromising performance
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