654 research outputs found
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
A large amount of information exists in reviews written by users. This source
of information has been ignored by most of the current recommender systems
while it can potentially alleviate the sparsity problem and improve the quality
of recommendations. In this paper, we present a deep model to learn item
properties and user behaviors jointly from review text. The proposed model,
named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel
neural networks coupled in the last layers. One of the networks focuses on
learning user behaviors exploiting reviews written by the user, and the other
one learns item properties from the reviews written for the item. A shared
layer is introduced on the top to couple these two networks together. The
shared layer enables latent factors learned for users and items to interact
with each other in a manner similar to factorization machine techniques.
Experimental results demonstrate that DeepCoNN significantly outperforms all
baseline recommender systems on a variety of datasets.Comment: WSDM 201
Kinetic models of ion transport through a nanopore
Kinetic equations for the stationary state distribution function of ions
moving through narrow pores are solved for a number of one-dimensional models
of single ion transport. Ions move through pores of length , under the
action of a constant external field and of a concentration gradient. The
interaction of single ions with the confining pore surface and with water
molecules inside the pore are modelled by a Fokker-Planck term in the kinetic
equation, or by uncorrelated collisions with thermalizing centres distributed
along the pore. The temporary binding of ions to polar residues lining the pore
is modelled by stopping traps or energy barriers. Analytic expressions for the
stationary ion current through the pore are derived for several versions of the
model, as functions of key physical parameters. In all cases, saturation of the
current at high fields is predicted. Such simple models, for which results are
analytic, may prove useful in the study of the current/voltage relations of ion
channels through membranes
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning
Various psychological factors affect how individuals express emotions. Yet,
when we collect data intended for use in building emotion recognition systems,
we often try to do so by creating paradigms that are designed just with a focus
on eliciting emotional behavior. Algorithms trained with these types of data
are unlikely to function outside of controlled environments because our
emotions naturally change as a function of these other factors. In this work,
we study how the multimodal expressions of emotion change when an individual is
under varying levels of stress. We hypothesize that stress produces modulations
that can hide the true underlying emotions of individuals and that we can make
emotion recognition algorithms more generalizable by controlling for variations
in stress. To this end, we use adversarial networks to decorrelate stress
modulations from emotion representations. We study how stress alters acoustic
and lexical emotional predictions, paying special attention to how modulations
due to stress affect the transferability of learned emotion recognition models
across domains. Our results show that stress is indeed encoded in trained
emotion classifiers and that this encoding varies across levels of emotions and
across the lexical and acoustic modalities. Our results also show that emotion
recognition models that control for stress during training have better
generalizability when applied to new domains, compared to models that do not
control for stress during training. We conclude that is is necessary to
consider the effect of extraneous psychological factors when building and
testing emotion recognition models.Comment: 10 pages, ICMI 201
Spatio-Temporal Fusion Networks for Action Recognition
The video based CNN works have focused on effective ways to fuse appearance
and motion networks, but they typically lack utilizing temporal information
over video frames. In this work, we present a novel spatio-temporal fusion
network (STFN) that integrates temporal dynamics of appearance and motion
information from entire videos. The captured temporal dynamic information is
then aggregated for a better video level representation and learned via
end-to-end training. The spatio-temporal fusion network consists of two set of
Residual Inception blocks that extract temporal dynamics and a fusion
connection for appearance and motion features. The benefits of STFN are: (a) it
captures local and global temporal dynamics of complementary data to learn
video-wide information; and (b) it is applicable to any network for video
classification to boost performance. We explore a variety of design choices for
STFN and verify how the network performance is varied with the ablation
studies. We perform experiments on two challenging human activity datasets,
UCF101 and HMDB51, and achieve the state-of-the-art results with the best
network
Simulations of a single membrane between two walls using a Monte Carlo method
Quantitative theory of interbilayer interactions is essential to interpret
x-ray scattering data and to elucidate these interactions for biologically
relevant systems. For this purpose Monte Carlo simulations have been performed
to obtain pressure P and positional fluctuations sigma. A new method, called
Fourier Monte-Carlo (FMC), that is based on a Fourier representation of the
displacement field, is developed and its superiority over the standard method
is demonstrated. The FMC method is applied to simulating a single membrane
between two hard walls, which models a stack of lipid bilayer membranes with
non-harmonic interactions. Finite size scaling is demonstrated and used to
obtain accurate values for P and sigma in the limit of a large continuous
membrane. The results are compared with perturbation theory approximations, and
numerical differences are found in the non-harmonic case. Therefore, the FMC
method, rather than the approximations, should be used for establishing the
connection between model potentials and observable quantities, as well as for
pure modeling purposes.Comment: 10 pages, 10 figure
A Deep Learning Parameterization for Ozone Dry Deposition Velocities
The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiala, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus similar to 0.1). The same DNN model, when driven by assimilated meteorology at 2 degrees x 2.5 degrees spatial resolution, outperforms the Wesely scheme as implemented in the GEOS-Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models. Plain Language Summary Ozone in the lower atmosphere is a toxic pollutant and greenhouse gas. In this work, we use a machine learning technique known as deep learning, to simulate the loss of ozone to Earth's surface. We show that our deep learning simulation of this loss process outperforms existing traditional models and demonstrate the opportunity for using machine learning to improve our understanding of the chemical composition of the atmosphere.Peer reviewe
Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks
Retinopathy of Prematurity (ROP) is an ocular disease observed in premature babies, considered one of the largest preventable causes of childhood blindness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP detector and with small modifications also return an approximate Bayesian posterior over disease presence. To the best of our knowledge, this is the first completely automated ROP detection system. (2) To further aid grading, we train a second CNN to return novel feature map visualizations of pathologies, learned directly from the data. These feature maps highlight discriminative information, which we believe may be used by clinicians with our classifier to aid in screening
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
The rising volume of datasets has made training machine learning (ML) models
a major computational cost in the enterprise. Given the iterative nature of
model and parameter tuning, many analysts use a small sample of their entire
data during their initial stage of analysis to make quick decisions (e.g., what
features or hyperparameters to use) and use the entire dataset only in later
stages (i.e., when they have converged to a specific model). This sampling,
however, is performed in an ad-hoc fashion. Most practitioners cannot precisely
capture the effect of sampling on the quality of their model, and eventually on
their decision-making process during the tuning phase. Moreover, without
systematic support for sampling operators, many optimizations and reuse
opportunities are lost.
In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML
training. BlinkML allows users to make error-computation tradeoffs: instead of
training a model on their full data (i.e., full model), BlinkML can quickly
train an approximate model with quality guarantees using a sample. The quality
guarantees ensure that, with high probability, the approximate model makes the
same predictions as the full model. BlinkML currently supports any ML model
that relies on maximum likelihood estimation (MLE), which includes Generalized
Linear Models (e.g., linear regression, logistic regression, max entropy
classifier, Poisson regression) as well as PPCA (Probabilistic Principal
Component Analysis). Our experiments show that BlinkML can speed up the
training of large-scale ML tasks by 6.26x-629x while guaranteeing the same
predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201
Volume-energy correlations in the slow degrees of freedom of computer-simulated phospholipid membranes
Constant-pressure molecular-dynamics simulations of phospholipid membranes in
the fluid phase reveal strong correlations between equilibrium fluctuations of
volume and energy on the nanosecond time-scale. The existence of strong
volume-energy correlations was previously deduced indirectly by Heimburg from
experiments focusing on the phase transition between the fluid and the ordered
gel phases. The correlations, which are reported here for three different
membranes (DMPC, DMPS-Na, and DMPSH), have volume-energy correlation
coefficients ranging from 0.81 to 0.89. The DMPC membrane was studied at two
temperatures showing that the correlation coefficient increases as the phase
transition is approached
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