1,510 research outputs found
Variational Deep Semantic Hashing for Text Documents
As the amount of textual data has been rapidly increasing over the past
decade, efficient similarity search methods have become a crucial component of
large-scale information retrieval systems. A popular strategy is to represent
original data samples by compact binary codes through hashing. A spectrum of
machine learning methods have been utilized, but they often lack expressiveness
and flexibility in modeling to learn effective representations. The recent
advances of deep learning in a wide range of applications has demonstrated its
capability to learn robust and powerful feature representations for complex
data. Especially, deep generative models naturally combine the expressiveness
of probabilistic generative models with the high capacity of deep neural
networks, which is very suitable for text modeling. However, little work has
leveraged the recent progress in deep learning for text hashing.
In this paper, we propose a series of novel deep document generative models
for text hashing. The first proposed model is unsupervised while the second one
is supervised by utilizing document labels/tags for hashing. The third model
further considers document-specific factors that affect the generation of
words. The probabilistic generative formulation of the proposed models provides
a principled framework for model extension, uncertainty estimation, simulation,
and interpretability. Based on variational inference and reparameterization,
the proposed models can be interpreted as encoder-decoder deep neural networks
and thus they are capable of learning complex nonlinear distributed
representations of the original documents. We conduct a comprehensive set of
experiments on four public testbeds. The experimental results have demonstrated
the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match
users to items (e.g. news, products, music, video). In these two-sided markets,
not only the users draw utility from the rankings, but the rankings also
determine the utility (e.g. exposure, revenue) for the item providers (e.g.
publishers, sellers, artists, studios). It has already been noted that
myopically optimizing utility to the users, as done by virtually all
learning-to-rank algorithms, can be unfair to the item providers. We,
therefore, present a learning-to-rank approach for explicitly enforcing
merit-based fairness guarantees to groups of items (e.g. articles by the same
publisher, tracks by the same artist). In particular, we propose a learning
algorithm that ensures notions of amortized group fairness, while
simultaneously learning the ranking function from implicit feedback data. The
algorithm takes the form of a controller that integrates unbiased estimators
for both fairness and utility, dynamically adapting both as more data becomes
available. In addition to its rigorous theoretical foundation and convergence
guarantees, we find empirically that the algorithm is highly practical and
robust.Comment: First two authors contributed equally. In Proceedings of the 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval 202
Learning Task Relatedness in Multi-Task Learning for Images in Context
Multimedia applications often require concurrent solutions to multiple tasks.
These tasks hold clues to each-others solutions, however as these relations can
be complex this remains a rarely utilized property. When task relations are
explicitly defined based on domain knowledge multi-task learning (MTL) offers
such concurrent solutions, while exploiting relatedness between multiple tasks
performed over the same dataset. In most cases however, this relatedness is not
explicitly defined and the domain expert knowledge that defines it is not
available. To address this issue, we introduce Selective Sharing, a method that
learns the inter-task relatedness from secondary latent features while the
model trains. Using this insight, we can automatically group tasks and allow
them to share knowledge in a mutually beneficial way. We support our method
with experiments on 5 datasets in classification, regression, and ranking tasks
and compare to strong baselines and state-of-the-art approaches showing a
consistent improvement in terms of accuracy and parameter counts. In addition,
we perform an activation region analysis showing how Selective Sharing affects
the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster
A GIS MODELLING APPROACH FOR FLOOD HAZARD ASSESSMENT IN PART OF SURAKARTA CITY, INDONESIA
This research is aimed to assess the flood hazard in part of Surakarta usinghydrodynamic modelling. Flo2D software is used to simulate the flood for 10, 25and 100 year return period. The modeling results include two flood parameters, i.ewater depth and flow velocity. A comparison was made in flood hazard mappingbetween single parameter and multi parameters. The multi parameters hazardmaps improve the reliability of the hazard class delineation. The impact assessmentis done in two point of view, human safety and property damage. The furtherimpact assessment is done by calculating the number of buildings affected by flood
GPCSIM : an instrument simulator of polymer analysis by size exclusion chromatography for demonstration and training purposes
A computer simulation has been developed with the purpose of demonstrating and visualizing a multitude of effects in the molecular characterization of synthetic polymer mixtures by size exclusion (gel permeation) chromatography. The chromatographic results and their interpretation are influenced by numerous parameters originating from sample, column and instrumentation used (injection, detection etc). The target audience for the software tool consists of polymers scientists, teachers of separation science and students. Especially for the latter audience it is important to stress that the software enables intentional creation of mistakes and learning from these mistakes. What the user can do ranges from visualization (quantitatively) all retention and dispersion effects, validation of experimental setup, checking sensitivity for certain operating conditions, extrapolation current instrument specifications, and in general performing hypothetical experiments. Several examples, such as column choice, band broadening, detection comparison and possible artifacts in the calculation of distributions are presented. This simulator is part of a family of similar tools for gas chromatography, high performance liquid chromatography, micellar electrokinetic chromatography and capillary electrophoresis. They have proved their effectiveness in education of separation science topics at several European universities
Transformation Pathways of Silica under High Pressure
Concurrent molecular dynamics simulations and ab initio calculations show
that densification of silica under pressure follows a ubiquitous two-stage
mechanism. First, anions form a close-packed sub-lattice, governed by the
strong repulsion between them. Next, cations redistribute onto the interstices.
In cristobalite silica, the first stage is manifest by the formation of a
metastable phase, which was observed experimentally a decade ago, but never
indexed due to ambiguous diffraction patterns. Our simulations conclusively
reveal its structure and its role in the densification of silica.Comment: 14 pages, 4 figure
Contribution of arm movements to balance recovery after tripping in older adults
\ua9 2022 The AuthorsFalls are common in daily life, often caused by trips and slips and, particularly in older adults, with serious consequences. Although arm movements play an important role in balance control, there is limited research into the role of arm movements during balance recovery after tripping in older adults. We investigated how older adults use their arms to recover from a trip and the difference in the effects of arm movements between fallers (n = 5) and non-fallers (n = 11). Sixteen older males and females (69.7 \ub1 2.3 years) walked along a walkway and were occasionally tripped over suddenly appearing obstacles. We analysed the first trip using a biomechanical model based on full-body kinematics and force-plate data to calculate whole body orientation during the trip and recovery phase. With this model, we simulated the effects of arm movements at foot-obstacle impact and during trip recovery on body orientation. Apart from an increase in sagittal plane forward body rotation at touchdown in fallers, we found no significant differences between fallers and non-fallers in the effects of arm movements on trip recovery. Like earlier studies in young adults, we found that arm movements during the recovery phase had most favourable effects in the transverse plane: by delaying the transfer of angular momentum of the arms to the body, older adults rotated the tripped side more forward thereby allowing for a larger recovery step. Older adults that are prone to falling might improve their balance recovery after tripping by learning to prolong ongoing arm movements
Learning the Structure of Auto-Encoding Recommenders
Autoencoder recommenders have recently shown state-of-the-art performance in
the recommendation task due to their ability to model non-linear item
relationships effectively. However, existing autoencoder recommenders use
fully-connected neural network layers and do not employ structure learning.
This can lead to inefficient training, especially when the data is sparse as
commonly found in collaborative filtering. The aforementioned results in lower
generalization ability and reduced performance. In this paper, we introduce
structure learning for autoencoder recommenders by taking advantage of the
inherent item groups present in the collaborative filtering domain. Due to the
nature of items in general, we know that certain items are more related to each
other than to other items. Based on this, we propose a method that first learns
groups of related items and then uses this information to determine the
connectivity structure of an auto-encoding neural network. This results in a
network that is sparsely connected. This sparse structure can be viewed as a
prior that guides the network training. Empirically we demonstrate that the
proposed structure learning enables the autoencoder to converge to a local
optimum with a much smaller spectral norm and generalization error bound than
the fully-connected network. The resultant sparse network considerably
outperforms the state-of-the-art methods like \textsc{Mult-vae/Mult-dae} on
multiple benchmarked datasets even when the same number of parameters and flops
are used. It also has a better cold-start performance.Comment: Proceedings of The Web Conference 202
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