858 research outputs found
Evolutionary estimation of a Coupled Markov Chain credit risk model
There exists a range of different models for estimating and simulating credit
risk transitions to optimally manage credit risk portfolios and products. In
this chapter we present a Coupled Markov Chain approach to model rating
transitions and thereby default probabilities of companies. As the likelihood
of the model turns out to be a non-convex function of the parameters to be
estimated, we apply heuristics to find the ML estimators. To this extent, we
outline the model and its likelihood function, and present both a Particle
Swarm Optimization algorithm, as well as an Evolutionary Optimization algorithm
to maximize the likelihood function. Numerical results are shown which suggest
a further application of evolutionary optimization techniques for credit risk
management
Neural Networks Compression for Language Modeling
In this paper, we consider several compression techniques for the language
modeling problem based on recurrent neural networks (RNNs). It is known that
conventional RNNs, e.g, LSTM-based networks in language modeling, are
characterized with either high space complexity or substantial inference time.
This problem is especially crucial for mobile applications, in which the
constant interaction with the remote server is inappropriate. By using the Penn
Treebank (PTB) dataset we compare pruning, quantization, low-rank
factorization, tensor train decomposition for LSTM networks in terms of model
size and suitability for fast inference.Comment: Keywords: LSTM, RNN, language modeling, low-rank factorization,
pruning, quantization. Published by Springer in the LNCS series, 7th
International Conference on Pattern Recognition and Machine Intelligence,
201
Learning Temporal Transformations From Time-Lapse Videos
Based on life-long observations of physical, chemical, and biologic phenomena
in the natural world, humans can often easily picture in their minds what an
object will look like in the future. But, what about computers? In this paper,
we learn computational models of object transformations from time-lapse videos.
In particular, we explore the use of generative models to create depictions of
objects at future times. These models explore several different prediction
tasks: generating a future state given a single depiction of an object,
generating a future state given two depictions of an object at different times,
and generating future states recursively in a recurrent framework. We provide
both qualitative and quantitative evaluations of the generated results, and
also conduct a human evaluation to compare variations of our models.Comment: ECCV201
A Neural Attention Model for Categorizing Patient Safety Events
Medical errors are leading causes of death in the US and as such, prevention
of these errors is paramount to promoting health care. Patient Safety Event
reports are narratives describing potential adverse events to the patients and
are important in identifying and preventing medical errors. We present a neural
network architecture for identifying the type of safety events which is the
first step in understanding these narratives. Our proposed model is based on a
soft neural attention model to improve the effectiveness of encoding long
sequences. Empirical results on two large-scale real-world datasets of patient
safety reports demonstrate the effectiveness of our method with significant
improvements over existing methods.Comment: ECIR 201
Evolutionary multi-stage financial scenario tree generation
Multi-stage financial decision optimization under uncertainty depends on a
careful numerical approximation of the underlying stochastic process, which
describes the future returns of the selected assets or asset categories.
Various approaches towards an optimal generation of discrete-time,
discrete-state approximations (represented as scenario trees) have been
suggested in the literature. In this paper, a new evolutionary algorithm to
create scenario trees for multi-stage financial optimization models will be
presented. Numerical results and implementation details conclude the paper
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table
Label-Dependencies Aware Recurrent Neural Networks
In the last few years, Recurrent Neural Networks (RNNs) have proved effective
on several NLP tasks. Despite such great success, their ability to model
\emph{sequence labeling} is still limited. This lead research toward solutions
where RNNs are combined with models which already proved effective in this
domain, such as CRFs. In this work we propose a solution far simpler but very
effective: an evolution of the simple Jordan RNN, where labels are re-injected
as input into the network, and converted into embeddings, in the same way as
words. We compare this RNN variant to all the other RNN models, Elman and
Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language
Understanding (SLU). Thanks to label embeddings and their combination at the
hidden layer, the proposed variant, which uses more parameters than Elman and
Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other
RNNs, but also outperforms sophisticated CRF models.Comment: 22 pages, 3 figures. Accepted at CICling 2017 conference. Best
Verifiability, Reproducibility, and Working Description awar
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Image-based camera relocalization is an important problem in computer vision
and robotics. Recent works utilize convolutional neural networks (CNNs) to
regress for pixels in a query image their corresponding 3D world coordinates in
the scene. The final pose is then solved via a RANSAC-based optimization scheme
using the predicted coordinates. Usually, the CNN is trained with ground truth
scene coordinates, but it has also been shown that the network can discover 3D
scene geometry automatically by minimizing single-view reprojection loss.
However, due to the deficiencies of the reprojection loss, the network needs to
be carefully initialized. In this paper, we present a new angle-based
reprojection loss, which resolves the issues of the original reprojection loss.
With this new loss function, the network can be trained without careful
initialization, and the system achieves more accurate results. The new loss
also enables us to utilize available multi-view constraints, which further
improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning
A convolution BiLSTM neural network model for Chinese event extraction
Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling
A Diagram Is Worth A Dozen Images
Diagrams are common tools for representing complex concepts, relationships
and events, often when it would be difficult to portray the same information
with natural images. Understanding natural images has been extensively studied
in computer vision, while diagram understanding has received little attention.
In this paper, we study the problem of diagram interpretation and reasoning,
the challenging task of identifying the structure of a diagram and the
semantics of its constituents and their relationships. We introduce Diagram
Parse Graphs (DPG) as our representation to model the structure of diagrams. We
define syntactic parsing of diagrams as learning to infer DPGs for diagrams and
study semantic interpretation and reasoning of diagrams in the context of
diagram question answering. We devise an LSTM-based method for syntactic
parsing of diagrams and introduce a DPG-based attention model for diagram
question answering. We compile a new dataset of diagrams with exhaustive
annotations of constituents and relationships for over 5,000 diagrams and
15,000 questions and answers. Our results show the significance of our models
for syntactic parsing and question answering in diagrams using DPGs
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