2,541 research outputs found
Learning Fashion Compatibility with Bidirectional LSTMs
The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.Comment: ACM MM 1
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer
Efficiency of some dimensionality reduction techniques, like lung
segmentation, bone shadow exclusion, and t-distributed stochastic neighbor
embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest
X-ray (CXR) 2D images by deep learning approach to help radiologists identify
marks of lung cancer in CXR. Training and validation of the simple
convolutional neural network (CNN) was performed on the open JSRT dataset
(dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02),
JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation
(dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE
method (dataset #05). The results demonstrate that the pre-processed dataset
obtained after lung segmentation, bone shadow exclusion, and filtering out the
outliers by t-SNE (dataset #05) demonstrates the highest training rate and best
accuracy in comparison to the other pre-processed datasets.Comment: 6 pages, 14 figure
Quality flags for GSP-Phot Gaia DR3 astrophysical parameters with machine learning: Effective temperatures case study
Gaia Data Release 3 (DR3) provides extensive information on the astrophysical
properties of stars, such as effective temperature, surface gravity,
metallicity, and luminosity, for over 470 million objects. However, as Gaia's
stellar parameters in GSP-Phot module are derived through model-dependent
methods and indirect measurements, it can lead to additional systematic errors
in the derived parameters. In this study, we compare GSP-Phot effective
temperature estimates with two high-resolution and high signal-to-noise
spectroscopic catalogues: APOGEE DR17 and GALAH DR3, aiming to assess the
reliability of Gaia's temperatures. We introduce an approach to distinguish
good-quality Gaia DR3 effective temperatures using machine-learning methods
such as XGBoost, CatBoost and LightGBM. The models create quality flags, which
can help one to distinguish good-quality GSP-Phot effective temperatures. We
test our models on three independent datasets, including PASTEL, a compilation
of spectroscopically derived stellar parameters from different high-resolution
studies. The results of the test suggest that with these models it is possible
to filter effective temperatures as accurate as 250 K with ~ 90 per cent
precision even in complex regions, such as the Galactic plane. Consequently,
the models developed herein offer a valuable quality assessment tool for
GSP-Phot effective temperatures in Gaia DR3. Consequently, the developed models
offer a valuable quality assessment tool for GSP-Phot effective temperatures in
Gaia DR3. The dataset with flags for all GSP-Phot effective temperature
estimates, is publicly available, as are the models themselves.Comment: 13 pages, 10 figure
Feature Cross Search via Submodular Optimization
In this paper, we study feature cross search as a fundamental primitive in
feature engineering. The importance of feature cross search especially for the
linear model has been known for a while, with well-known textbook examples. In
this problem, the goal is to select a small subset of features, combine them to
form a new feature (called the crossed feature) by considering their Cartesian
product, and find feature crosses to learn an \emph{accurate} model. In
particular, we study the problem of maximizing a normalized Area Under the
Curve (AUC) of the linear model trained on the crossed feature column.
First, we show that it is not possible to provide an -approximation algorithm for this problem unless the exponential time
hypothesis fails. This result also rules out the possibility of solving this
problem in polynomial time unless . On the positive
side, by assuming the \naive\ assumption, we show that there exists a simple
greedy -approximation algorithm for this problem. This result is
established by relating the AUC to the total variation of the commutator of two
probability measures and showing that the total variation of the commutator is
monotone and submodular. To show this, we relate the submodularity of this
function to the positive semi-definiteness of a corresponding kernel matrix.
Then, we use Bochner's theorem to prove the positive semi-definiteness by
showing that its inverse Fourier transform is non-negative everywhere. Our
techniques and structural results might be of independent interest.Comment: Accepted to ESA 2021. Authors are ordered alphabeticall
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