7,464 research outputs found
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Continuous/Lifelong learning of high-dimensional data streams is a
challenging research problem. In fact, fully retraining models each time new
data become available is infeasible, due to computational and storage issues,
while na\"ive incremental strategies have been shown to suffer from
catastrophic forgetting. In the context of real-world object recognition
applications (e.g., robotic vision), where continuous learning is crucial, very
few datasets and benchmarks are available to evaluate and compare emerging
techniques. In this work we propose a new dataset and benchmark CORe50,
specifically designed for continuous object recognition, and introduce baseline
approaches for different continuous learning scenarios
Efficient posterior sampling for high-dimensional imbalanced logistic regression
High-dimensional data are routinely collected in many areas. We are
particularly interested in Bayesian classification models in which one or more
variables are imbalanced. Current Markov chain Monte Carlo algorithms for
posterior computation are inefficient as and/or increase due to
worsening time per step and mixing rates. One strategy is to use a
gradient-based sampler to improve mixing while using data sub-samples to reduce
per-step computational complexity. However, usual sub-sampling breaks down when
applied to imbalanced data. Instead, we generalize piece-wise deterministic
Markov chain Monte Carlo algorithms to include importance-weighted and
mini-batch sub-sampling. These approaches maintain the correct stationary
distribution with arbitrarily small sub-samples, and substantially outperform
current competitors. We provide theoretical support and illustrate gains in
simulated and real data applications.Comment: 4 figure
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Deep Learning (DL) models are becoming larger, because the increase in model
size might offer significant accuracy gain. To enable the training of large
deep networks, data parallelism and model parallelism are two well-known
approaches for parallel training. However, data parallelism does not help
reduce memory footprint per device. In this work, we introduce Large deep 3D
ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of
both input's and deep 3D ConvNets' size on segmentation accuracy. Through
automated model parallelism, it is feasible to train large deep 3D ConvNets
with a large input patch, even the whole image. Extensive experiments
demonstrate that, facilitated by the automated model parallelism, the
segmentation accuracy can be improved through increasing model size and input
context size, and large input yields significant inference speedup compared
with sliding window of small patches in the inference. Code is
available\footnote{https://monai.io/research/lamp-automated-model-parallelism}.Comment: MICCAI 2020 Early Accepted paper. Code is
available\footnote{https://monai.io/research/lamp-automated-model-parallelism
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
This paper presents a novel algorithm, based upon the dependent Dirichlet
process mixture model (DDPMM), for clustering batch-sequential data containing
an unknown number of evolving clusters. The algorithm is derived via a
low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM,
and provides a hard clustering with convergence guarantees similar to those of
the k-means algorithm. Empirical results from a synthetic test with moving
Gaussian clusters and a test with real ADS-B aircraft trajectory data
demonstrate that the algorithm requires orders of magnitude less computational
time than contemporary probabilistic and hard clustering algorithms, while
providing higher accuracy on the examined datasets.Comment: This paper is from NIPS 2013. Please use the following BibTeX
citation: @inproceedings{Campbell13_NIPS, Author = {Trevor Campbell and Miao
Liu and Brian Kulis and Jonathan P. How and Lawrence Carin}, Title = {Dynamic
Clustering via Asymptotics of the Dependent Dirichlet Process}, Booktitle =
{Advances in Neural Information Processing Systems (NIPS)}, Year = {2013}
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