17,695 research outputs found
Counting points on hyperelliptic curves with explicit real multiplication in arbitrary genus
We present a probabilistic Las Vegas algorithm for computing the local zeta
function of a genus- hyperelliptic curve defined over with
explicit real multiplication (RM) by an order in a degree-
totally real number field.
It is based on the approaches by Schoof and Pila in a more favorable case
where we can split the -torsion into kernels of endomorphisms, as
introduced by Gaudry, Kohel, and Smith in genus 2. To deal with these kernels
in any genus, we adapt a technique that the author, Gaudry, and Spaenlehauer
introduced to model the -torsion by structured polynomial systems.
Applying this technique to the kernels, the systems we obtain are much smaller
and so is the complexity of solving them.
Our main result is that there exists a constant such that, for any
fixed , this algorithm has expected time and space complexity as grows and the characteristic is large enough. We prove that
and we also conjecture that the result still holds for .Comment: To appear in Journal of Complexity. arXiv admin note: text overlap
with arXiv:1710.0344
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
Multi-scale representations deeply learned via convolutional neural networks
have shown tremendous importance for various pixel-level prediction problems.
In this paper we present a novel approach that advances the state of the art on
pixel-level prediction in a fundamental aspect, i.e. structured multi-scale
features learning and fusion. In contrast to previous works directly
considering multi-scale feature maps obtained from the inner layers of a
primary CNN architecture, and simply fusing the features with weighted
averaging or concatenation, we propose a probabilistic graph attention network
structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs)
model for learning and fusing multi-scale representations in a principled
manner. In order to further improve the learning capacity of the network
structure, we propose to exploit feature dependant conditional kernels within
the deep probabilistic framework. Extensive experiments are conducted on four
publicly available datasets (i.e. BSDS500, NYUD-V2, KITTI, and Pascal-Context)
and on three challenging pixel-wise prediction problems involving both discrete
and continuous labels (i.e. monocular depth estimation, object contour
prediction, and semantic segmentation). Quantitative and qualitative results
demonstrate the effectiveness of the proposed latent AG-CRF model and the
overall probabilistic graph attention network with feature conditional kernels
for structured feature learning and pixel-wise prediction.Comment: Regular paper accepted at TPAMI 2020. arXiv admin note: text overlap
with arXiv:1801.0052
Combining Thesaurus Knowledge and Probabilistic Topic Models
In this paper we present the approach of introducing thesaurus knowledge into
probabilistic topic models. The main idea of the approach is based on the
assumption that the frequencies of semantically related words and phrases,
which are met in the same texts, should be enhanced: this action leads to their
larger contribution into topics found in these texts. We have conducted
experiments with several thesauri and found that for improving topic models, it
is useful to utilize domain-specific knowledge. If a general thesaurus, such as
WordNet, is used, the thesaurus-based improvement of topic models can be
achieved with excluding hyponymy relations in combined topic models.Comment: Accepted to AIST-2017 conference (http://aistconf.ru/). The final
publication will be available at link.springer.co
Transductive Learning with String Kernels for Cross-Domain Text Classification
For many text classification tasks, there is a major problem posed by the
lack of labeled data in a target domain. Although classifiers for a target
domain can be trained on labeled text data from a related source domain, the
accuracy of such classifiers is usually lower in the cross-domain setting.
Recently, string kernels have obtained state-of-the-art results in various text
classification tasks such as native language identification or automatic essay
scoring. Moreover, classifiers based on string kernels have been found to be
robust to the distribution gap between different domains. In this paper, we
formally describe an algorithm composed of two simple yet effective
transductive learning approaches to further improve the results of string
kernels in cross-domain settings. By adapting string kernels to the test set
without using the ground-truth test labels, we report significantly better
accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap
with arXiv:1808.0840
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
Exploring Prediction Uncertainty in Machine Translation Quality Estimation
Machine Translation Quality Estimation is a notoriously difficult task, which
lessens its usefulness in real-world translation environments. Such scenarios
can be improved if quality predictions are accompanied by a measure of
uncertainty. However, models in this task are traditionally evaluated only in
terms of point estimate metrics, which do not take prediction uncertainty into
account. We investigate probabilistic methods for Quality Estimation that can
provide well-calibrated uncertainty estimates and evaluate them in terms of
their full posterior predictive distributions. We also show how this posterior
information can be useful in an asymmetric risk scenario, which aims to capture
typical situations in translation workflows.Comment: Proceedings of CoNLL 201
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
We study the problem of synthesizing a number of likely future frames from a
single input image. In contrast to traditional methods, which have tackled this
problem in a deterministic or non-parametric way, we propose a novel approach
that models future frames in a probabilistic manner. Our probabilistic model
makes it possible for us to sample and synthesize many possible future frames
from a single input image. Future frame synthesis is challenging, as it
involves low- and high-level image and motion understanding. We propose a novel
network structure, namely a Cross Convolutional Network to aid in synthesizing
future frames; this network structure encodes image and motion information as
feature maps and convolutional kernels, respectively. In experiments, our model
performs well on synthetic data, such as 2D shapes and animated game sprites,
as well as on real-wold videos. We also show that our model can be applied to
tasks such as visual analogy-making, and present an analysis of the learned
network representations.Comment: The first two authors contributed equally to this wor
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