4,251 research outputs found
Neural Ideal Point Estimation Network
Understanding politics is challenging because the politics take the influence
from everything. Even we limit ourselves to the political context in the
legislative processes; we need a better understanding of latent factors, such
as legislators, bills, their ideal points, and their relations. From the
modeling perspective, this is difficult 1) because these observations lie in a
high dimension that requires learning on low dimensional representations, and
2) because these observations require complex probabilistic modeling with
latent variables to reflect the causalities. This paper presents a new model to
reflect and understand this political setting, NIPEN, including factors
mentioned above in the legislation. We propose two versions of NIPEN: one is a
hybrid model of deep learning and probabilistic graphical model, and the other
model is a neural tensor model. Our result indicates that NIPEN successfully
learns the manifold of the legislative bill texts, and NIPEN utilizes the
learned low-dimensional latent variables to increase the prediction performance
of legislators' votings. Additionally, by virtue of being a domain-rich
probabilistic model, NIPEN shows the hidden strength of the legislators' trust
network and their various characteristics on casting votes
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the
embedding space has experienced a breakthrough in recent years. In spite of the
advance, clustering with representation learning has been limited to flat-level
categories, which often involves cohesive clustering with a focus on instance
relations. To overcome the limitations of flat clustering, we introduce
hierarchically-clustered representation learning (HCRL), which simultaneously
optimizes representation learning and hierarchical clustering in the embedding
space. Compared with a few prior works, HCRL firstly attempts to consider a
generation of deep embeddings from every component of the hierarchy, not just
leaf components. In addition to obtaining hierarchically clustered embeddings,
we can reconstruct data by the various abstraction levels, infer the intrinsic
hierarchical structure, and learn the level-proportion features. We conducted
evaluations with image and text domains, and our quantitative analyses showed
competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape
Hierarchical Context enabled Recurrent Neural Network for Recommendation
A long user history inevitably reflects the transitions of personal interests
over time. The analyses on the user history require the robust sequential model
to anticipate the transitions and the decays of user interests. The user
history is often modeled by various RNN structures, but the RNN structures in
the recommendation system still suffer from the long-term dependency and the
interest drifts. To resolve these challenges, we suggest HCRNN with three
hierarchical contexts of the global, the local, and the temporary interests.
This structure is designed to withhold the global long-term interest of users,
to reflect the local sub-sequence interests, and to attend the temporary
interests of each transition. Besides, we propose a hierarchical context-based
gate structure to incorporate our \textit{interest drift assumption}. As we
suggest a new RNN structure, we support HCRNN with a complementary
\textit{bi-channel attention} structure to utilize hierarchical context. We
experimented the suggested structure on the sequential recommendation tasks
with CiteULike, MovieLens, and LastFM, and our model showed the best
performances in the sequential recommendations
Bivariate Beta-LSTM
Long Short-Term Memory (LSTM) infers the long term dependency through a cell
state maintained by the input and the forget gate structures, which models a
gate output as a value in [0,1] through a sigmoid function. However, due to the
graduality of the sigmoid function, the sigmoid gate is not flexible in
representing multi-modality or skewness. Besides, the previous models lack
modeling on the correlation between the gates, which would be a new method to
adopt inductive bias for a relationship between previous and current input.
This paper proposes a new gate structure with the bivariate Beta distribution.
The proposed gate structure enables probabilistic modeling on the gates within
the LSTM cell so that the modelers can customize the cell state flow with
priors and distributions. Moreover, we theoretically show the higher upper
bound of the gradient compared to the sigmoid function, and we empirically
observed that the bivariate Beta distribution gate structure provides higher
gradient values in training. We demonstrate the effectiveness of bivariate Beta
gate structure on the sentence classification, image classification, polyphonic
music modeling, and image caption generation.Comment: AAAI 202
The Relationship between School Shootings and Gun Acquisition Rates
In this paper, I seek to understand how communities respond to tragic events involving violence, exploring the relationship between school shootings and gun acquisition rates. Using National Instant Criminal Background Check System (NICS) as a proxy for firearm acquisition rates, I estimate an event study framework, finding that gun acquisition rates increase by up to 32% one month after a school shooting compared to firearm acquisition rates one month before a school shooting. Furthermore, I supplement my analysis by using Google Search data on firearms. Additionally, I stratify my analysis by the four census regions and whether a school shooting occurred in a majority-minority county. My results contribute to existing literature, investigating the linkages between Google search data and social phenomena and the impact of mass shootings on the social sphere
Critical Velocity for Vortex Shedding in a Bose-Einstein Condensate
We present measurements of the critical velocity for vortex shedding in a
highly oblate Bose-Einstein condensate with a moving repulsive Gaussian laser
beam. As a function of the barrier height , the critical velocity
shows a dip structure having a minimum at , where is
the chemical potential of the condensate. At fixed , we
observe that the ratio of to the speed of sound monotonically
increases for decreasing , where is the beam width and
is the condensate healing length. The measured upper bound for
is about 0.4, which is in good agreement with theoretical predictions for a
two-dimensional superflow past a circular cylinder. We explain our results with
the density reduction effect of the soft boundary of the Gaussian obstacle,
based on the local Landau criterion for superfluidity.Comment: 5 pages, 4 figure
Adversarial Dropout for Supervised and Semi-supervised Learning
Recently, the training with adversarial examples, which are generated by
adding a small but worst-case perturbation on input examples, has been proved
to improve generalization performance of neural networks. In contrast to the
individually biased inputs to enhance the generality, this paper introduces
adversarial dropout, which is a minimal set of dropouts that maximize the
divergence between the outputs from the network with the dropouts and the
training supervisions. The identified adversarial dropout are used to
reconfigure the neural network to train, and we demonstrated that training on
the reconfigured sub-network improves the generalization performance of
supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We
analyzed the trained model to reason the performance improvement, and we found
that adversarial dropout increases the sparsity of neural networks more than
the standard dropout does.Comment: submitted to AAAI-1
Management of a Single-User Multi-Robot Teleoperated System for Maintenance in Offshore Plants
This chapter proposes a new approach to management method of a single-user multi-robot teleoperated system for maintenance in offshore plants. The management method is designed to perform a 1:N mode (here, “1” refers to the number of operators and “N” denotes the number of slave robots), in which a single operator teleoperates a number of slave robots directly to conduct a maintenance task, or in an autonomous cooperation mode between slave robots in order to overcome the limitations of the aforementioned 1:1 teleoperation mode. The aforementioned management method is responsible for the role sharing and integration of slave robots to divide the operation mode of the slave robots into various types according to the operator’s intervention level and the characteristics of the target maintenance task beforehand and to perform the target maintenance task using the robot operation mode selected by the operator
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