21,119 research outputs found
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation
A natural language interface (NLI) to structured query is intriguing due to
its wide industrial applications and high economical values. In this work, we
tackle the problem of domain adaptation for NLI with limited data on target
domain. Two important approaches are considered: (a) effective
general-knowledge-learning on source domain semantic parsing, and (b) data
augmentation on target domain. We present a Structured Query Inference Network
(SQIN) to enhance learning for domain adaptation, by separating schema
information from NL and decoding SQL in a more structural-aware manner; we also
propose a GAN-based augmentation technique (AugmentGAN) to mitigate the issue
of lacking target domain data. We report solid results on GeoQuery, Overnight,
and WikiSQL to demonstrate state-of-the-art performances for both in-domain and
domain-transfer tasks.Comment: 8 pages, 3 figures; accepted by AAAI Workshop 2019; accepted by
International Conference of Semantic Computing (ICSC) 201
"Synchronize" to VR Body: Full Body Illusion in VR Space
Virtual Reality (VR) becomes accessible to mimic a "real-like" world now.
People who have a VR experience usually can be impressed by the immersive
feeling, they might consider themselves are actually existed in the VR space.
Self-consciousness is important for people to identify their own characters in
VR space, and illusory ownership can help people to "build" their "bodies". The
rubber hand illusion can convince us a fake hand made by rubber is a part of
our bodies under certain circumstances. Researches about autoscopic phenomena
extend this illusory to the so-called full body illusion. We conducted 3 type
of experiments to study the illusory ownership in VR space as it shows in
Figure 1, and we learned: Human body must receive the synchronized visual
signal and somatosensory stimulus at the same time; The visual signal must be
the first person perceptive; the subject and the virtual body needs to be the
same height as much as possible. All these illusory ownerships accompanied by
the body temperature decreases, where the body is stimulated.Comment: 4 pages, 4 figures, Eurographics 2017,Conference short pape
Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images
Recently, a successful pose estimation algorithm, called Cascade Pose
Regression (CPR), was proposed in the literature. Trained over Pose Index
Feature, CPR is a regressor ensemble that is similar to Boosting. In this paper
we show how CPR can be represented as a Neural Network. Specifically, we adopt
a Graph Transformer Network (GTN) representation and accordingly train CPR with
Back Propagation (BP) that permits globally tuning. In contrast, previous CPR
literature only took a layer wise training without any post fine tuning. We
empirically show that global training with BP outperforms layer-wise
(pre-)training. Our CPR-GTN adopts a Multi Layer Percetron as the regressor,
which utilized sparse connection to learn local image feature representation.
We tested the proposed CPR-GTN on 2D face pose estimation problem as in
previous CPR literature. Besides, we also investigated the possibility of
extending CPR-GTN to 3D pose estimation by doing experiments using 3D Computed
Tomography dataset for heart segmentation
Study on the yields and polarizations of within the framework of non-relativistic QCD via at CEPC
Within the framework of the non-relativistic QCD (NRQCD), we make a
systematical study of the yields and polarizations of and
via in photon-photon collisions
at the Circular Electron Positron Collider (CEPC), up to . We find that this process at CEPC is quite "clean",
namely the direct photoproduction absolutely dominate over the single- and
double- resolved processes, at least 2 orders of magnitude larger. It is found
that the next-to-leading order (NLO) QCD corrections will significantly reduce
the results due to that the virtual corrections to is large and
negative. For , as increases, the color octet (CO) processes will
provide increasingly important contributions to the total NLO results. Moreover
the inclusion of CO contributions will dramatically change the polarizations of
from toally transverse to longitudinal, which can be regarded as a
distinct signal for the CO mechanism. However, for the case of , the
effects of the CO processes are negligible, both for yields and polarizations.
For , the dependence of the yields on the value of the renormalization
scale is moderate, while significant for the polarization. The impact
of the variation of is found to be relatively slight. As for
the case of , the uncertainties of and just
bring about negligible effects. The future measurements on this semi-inclusive
photoproductions of , especially on the polarization
parameters of , will be a good laboratory for the study of heavy
quarkonium production mechanism and helpful to clarify the problems of the
polarization puzzle
Kahler-Einstein metrics on Fano manifolds, I: approximation of metrics with cone singularities
This is the first of a series of three papers which provide proofs of results
announced recently in arXiv:1210.7494
Kahler-Einstein metrics and stability
We annnounce a proof of the fact that a K-stable Fano manifold admits a
Kahler-Einstein metric and give a brief outline of the proof
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
This paper introduces an extremely efficient CNN architecture named DFANet
for semantic segmentation under resource constraints. Our proposed network
starts from a single lightweight backbone and aggregates discriminative
features through sub-network and sub-stage cascade respectively. Based on the
multi-scale feature propagation, DFANet substantially reduces the number of
parameters, but still obtains sufficient receptive field and enhances the model
learning ability, which strikes a balance between the speed and segmentation
performance. Experiments on Cityscapes and CamVid datasets demonstrate the
superior performance of DFANet with 8 less FLOPs and 2 faster
than the existing state-of-the-art real-time semantic segmentation methods
while providing comparable accuracy. Specifically, it achieves 70.3\% Mean IOU
on the Cityscapes test dataset with only 1.7 GFLOPs and a speed of 160 FPS on
one NVIDIA Titan X card, and 71.3\% Mean IOU with 3.4 GFLOPs while inferring on
a higher resolution image
Soft Factor Subtraction and Transverse Momentum Dependent Parton Distributions on Lattice
We study the transverse momentum dependent (TMD) parton distributions in the
newly proposed quasi-parton distribution function framework in Euclidean space.
A soft factor subtraction is found to be essential to make the TMDs calculable
on lattice. We show that the quasi-TMDs with the associated soft factor
subtraction can be applied in hard QCD scattering processes such as Drell-Yan
lepton pair production in hadronic collisions. This allows future lattice
calculations to provide information on the non-perturbative inputs and energy
evolutions for the TMDs. Extension to the generalized parton distributions and
quantum phase space Wigner distributions will lead to a complete nucleon
tomography on lattice.Comment: 7 pages, 2 figure
Boosting with Structural Sparsity: A Differential Inclusion Approach
Boosting as gradient descent algorithms is one popular method in machine
learning. In this paper a novel Boosting-type algorithm is proposed based on
restricted gradient descent with structural sparsity control whose underlying
dynamics are governed by differential inclusions. In particular, we present an
iterative regularization path with structural sparsity where the parameter is
sparse under some linear transforms, based on variable splitting and the
Linearized Bregman Iteration. Hence it is called \emph{Split LBI}. Despite its
simplicity, Split LBI outperforms the popular generalized Lasso in both theory
and experiments. A theory of path consistency is presented that equipped with a
proper early stopping, Split LBI may achieve model selection consistency under
a family of Irrepresentable Conditions which can be weaker than the necessary
and sufficient condition for generalized Lasso. Furthermore, some
error bounds are also given at the minimax optimal rates. The utility and
benefit of the algorithm are illustrated by several applications including
image denoising, partial order ranking of sport teams, and world university
grouping with crowdsourced ranking data
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been
proposed to achieve a rapid training process with an element-wise scaling term
on learning rates. Though prevailing, they are observed to generalize poorly
compared with SGD or even fail to converge due to unstable and extreme learning
rates. Recent work has put forward some algorithms such as AMSGrad to tackle
this issue but they failed to achieve considerable improvement over existing
methods. In our paper, we demonstrate that extreme learning rates can lead to
poor performance. We provide new variants of Adam and AMSGrad, called AdaBound
and AMSBound respectively, which employ dynamic bounds on learning rates to
achieve a gradual and smooth transition from adaptive methods to SGD and give a
theoretical proof of convergence. We further conduct experiments on various
popular tasks and models, which is often insufficient in previous work.
Experimental results show that new variants can eliminate the generalization
gap between adaptive methods and SGD and maintain higher learning speed early
in training at the same time. Moreover, they can bring significant improvement
over their prototypes, especially on complex deep networks. The implementation
of the algorithm can be found at https://github.com/Luolc/AdaBound .Comment: Accepted to ICLR 2019. arXiv admin note: text overlap with
arXiv:1904.09237 by other author
- …