1,798 research outputs found
Efficient Learning for Undirected Topic Models
Replicated Softmax model, a well-known undirected topic model, is powerful in
extracting semantic representations of documents. Traditional learning
strategies such as Contrastive Divergence are very inefficient. This paper
provides a novel estimator to speed up the learning based on Noise Contrastive
Estimate, extended for documents of variant lengths and weighted inputs.
Experiments on two benchmarks show that the new estimator achieves great
learning efficiency and high accuracy on document retrieval and classification.Comment: Accepted by ACL-IJCNLP 2015 short paper. 6 page
Learning to Translate in Real-time with Neural Machine Translation
Translating in real-time, a.k.a. simultaneous translation, outputs
translation words before the input sentence ends, which is a challenging
problem for conventional machine translation methods. We propose a neural
machine translation (NMT) framework for simultaneous translation in which an
agent learns to make decisions on when to translate from the interaction with a
pre-trained NMT environment. To trade off quality and delay, we extensively
explore various targets for delay and design a method for beam-search
applicable in the simultaneous MT setting. Experiments against state-of-the-art
baselines on two language pairs demonstrate the efficacy of the proposed
framework both quantitatively and qualitatively.Comment: 10 pages, camera read
Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification
Data-driven modeling is useful for reconstructing nonlinear dynamical systems
when the underlying process is unknown or too expensive to compute. Having
reliable uncertainty assessment of the forecast enables tools to be deployed to
predict new scenarios unobserved before. In this work, we first extend parallel
partial Gaussian processes for predicting the vector-valued transition function
that links the observations between the current and next time points, and
quantify the uncertainty of predictions by posterior sampling. Second, we show
the equivalence between the dynamic mode decomposition and the maximum
likelihood estimator of the linear mapping matrix in the linear state space
model. The connection provides a data generating model of dynamic mode
decomposition and thus, uncertainty of predictions can be obtained.
Furthermore, we draw close connections between different data-driven models for
approximating nonlinear dynamics, through a unified view of data generating
models. We study two numerical examples, where the inputs of the dynamics are
assumed to be known in the first example and the inputs are unknown in the
second example. The examples indicate that uncertainty of forecast can be
properly quantified, whereas model or input misspecification can degrade the
accuracy of uncertainty quantification
Run ‐ to ‐Run Control of ITO Deposition Process
This paper describes the design and development of run‐to‐run control solution for an Indium Tin Oxide (ITO) deposition process. ITO deposition is an inherently complex process making it hard to simultaneously optimize the many characteristics of the ITO film such as optical transmission, resistivity, stresses in the film etc. With the run‐to‐run control solution, post‐process measurements made after every run are used along with empirical process models and drift compensation and noise rejection techniques to suggest new equipment settings for the next run. Theoretical models and simulation results show that this approach gives very stable ITO characteristics. Some of the methods that improve the control algorithm are discussed and future work is explored.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92093/1/1.1833812.pd
Migrating from Microservices to Serverless: An IoT Platform Case Study
Microservice architecture is the common choice for developing cloud
applications these days since each individual microservice can be independently
modified, replaced, and scaled. As a result, application development and
operating cloud infrastructure were bundled together into what is now commonly
called DevOps. However, with the increasing popularity of the serverless
computing paradigm and its several advantages such as no infrastructure
management, a pay-per-use billing policy, and on-demand fine-grained
autoscaling, there is a growing interest in utilizing FaaS and serverless CaaS
technologies for refactoring microservices-based applications. Towards this, we
migrate a complex IoT platform application onto OpenWhisk (OW) and Google Cloud
Run (GCR). We comprehensively evaluate the performance of the different
deployment strategies, i.e., Google Kubernetes Engine (GKE)-Standard, OW, and
GCR for the IoT platform using different load testing scenarios. Results from
our experiments show that while GKE standard performs best for most scenarios,
GCR is always cheaper wrt costs.Comment: ACM International Workshop on Serverless Computing 2022
(WoSC@Middleware 2022
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