11,365 research outputs found
Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach
Quantitative investment is a fundamental financial task that highly relies on
accurate stock prediction and profitable investment decision making. Despite
recent advances in deep learning (DL) have shown stellar performance on
capturing trading opportunities in the stochastic stock market, we observe that
the performance of existing DL methods is sensitive to random seeds and network
initialization. To design more profitable DL methods, we analyze this
phenomenon and find two major limitations of existing works. First, there is a
noticeable gap between accurate financial predictions and profitable investment
strategies. Second, investment decisions are made based on only one individual
predictor without consideration of model uncertainty, which is inconsistent
with the workflow in real-world trading firms. To tackle these two limitations,
we first reformulate quantitative investment as a multi-task learning problem.
Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE)
framework for quantitative investment to mimic the efficient bottom-up trading
strategy design workflow of successful trading firms. In Stage one, multiple
independent trading experts are jointly optimized with an individual
uncertainty-aware loss function. In Stage two, we train neural routers
(corresponding to the role of a portfolio manager) to dynamically deploy these
experts on an as-needed basis. AlphaMix is also a universal framework that is
applicable to various backbone network architectures with consistent
performance gains. Through extensive experiments on long-term real-world data
spanning over five years on two of the most influential financial markets (US
and China), we demonstrate that AlphaMix significantly outperforms many
state-of-the-art baselines in terms of four financial criteria
Understanding Hidden Memories of Recurrent Neural Networks
Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2017
A new approach for improving coronary plaque component analysis based on intravascular ultrasound images
Virtual histology intravascular ultrasound (VH-IVUS) is a clinically available technique for atherosclerosis plaque characterization. It, however, suffers from a poor longitudinal resolution due to electrocardiogram (ECG)-gated acquisition. This article presents an effective algorithm for IVUS image-based histology to overcome this limitation. After plaque area extraction within an input IVUS image, a textural analysis procedure consisting of feature extraction and classification steps is proposed. The pixels of the extracted plaque area excluding the shadow region were classified into one of the three plaque components of fibro-fatty (FF), calcification (CA) or necrotic core (NC) tissues. The average classification accuracy for pixel and region based validations is 75% and 87% respectively. Sensitivities (specificities) were 79% (85%) for CA, 81% (90%) for FF and 52% (82%) for NC. The kappa (kappa) = 0.61 and p value = 0.02 indicate good agreement of the proposed method with VH images. Finally, the enhancement in the longitudinal resolution was evaluated by reconstructing the IVUS images between the two sequential IVUS-VH images
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Understanding temporal dynamics has proved to be highly valuable for accurate
recommendation. Sequential recommenders have been successful in modeling the
dynamics of users and items over time. However, while different model
architectures excel at capturing various temporal ranges or dynamics, distinct
application contexts require adapting to diverse behaviors. In this paper we
examine how to build a model that can make use of different temporal ranges and
dynamics depending on the request context. We begin with the analysis of an
anonymized Youtube dataset comprising millions of user sequences. We quantify
the degree of long-range dependence in these sequences and demonstrate that
both short-term and long-term dependent behavioral patterns co-exist. We then
propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution
to deal with both short-term and long-term dependencies. Our approach employs a
mixture of models, each with a different temporal range. These models are
combined by a learned gating mechanism capable of exerting different model
combinations given different contextual information. In empirical evaluations
on a public dataset and our own anonymized YouTube dataset, M3 consistently
outperforms state-of-the-art sequential recommendation methods.Comment: Accepted at WWW 201
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
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