312 research outputs found
Identifying Patient Groups based on Frequent Patterns of Patient Samples
Grouping patients meaningfully can give insights about the different types of
patients, their needs, and the priorities. Finding groups that are meaningful
is however very challenging as background knowledge is often required to
determine what a useful grouping is. In this paper we propose an approach that
is able to find groups of patients based on a small sample of positive examples
given by a domain expert. Because of that, the approach relies on very limited
efforts by the domain experts. The approach groups based on the activities and
diagnostic/billing codes within health pathways of patients. To define such a
grouping based on the sample of patients efficiently, frequent patterns of
activities are discovered and used to measure the similarity between the care
pathways of other patients to the patients in the sample group. This approach
results in an insightful definition of the group. The proposed approach is
evaluated using several datasets obtained from a large university medical
center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury
and around 0.6 for diabetes
A Framework for Imbalanced Time-Series Forecasting
Time-series forecasting plays an important role in many domains. Boosted by
the advances in Deep Learning algorithms, it has for instance been used to
predict wind power for eolic energy production, stock market fluctuations, or
motor overheating. In some of these tasks, we are interested in predicting
accurately some particular moments which often are underrepresented in the
dataset, resulting in a problem known as imbalanced regression. In the
literature, while recognized as a challenging problem, limited attention has
been devoted on how to handle the problem in a practical setting. In this
paper, we put forward a general approach to analyze time-series forecasting
problems focusing on those underrepresented moments to reduce imbalances. Our
approach has been developed based on a case study in a large industrial
company, which we use to exemplify the approach
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study
Coping with distributional shifts is an important part of transfer learning
methods in order to perform well in real-life tasks. However, most of the
existing approaches in this area either focus on an ideal scenario in which the
data does not contain noises or employ a complicated training paradigm or model
design to deal with distributional shifts. In this paper, we revisit the
robustness of the minimum error entropy (MEE) criterion, a widely used
objective in statistical signal processing to deal with non-Gaussian noises,
and investigate its feasibility and usefulness in real-life transfer learning
regression tasks, where distributional shifts are common. Specifically, we put
forward a new theoretical result showing the robustness of MEE against
covariate shift. We also show that by simply replacing the mean squared error
(MSE) loss with the MEE on basic transfer learning algorithms such as
fine-tuning and linear probing, we can achieve competitive performance with
respect to state-of-the-art transfer learning algorithms. We justify our
arguments on both synthetic data and 5 real-world time-series data.Comment: Manuscript accepted at ECAI-23. Code available at
https://github.com/lpsilvestrin/mee-finetun
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS
Nowadays, with the rising number of sensors in sectors such as healthcare and
industry, the problem of multivariate time series classification (MTSC) is
getting increasingly relevant and is a prime target for machine and deep
learning approaches. Their expanding adoption in real-world environments is
causing a shift in focus from the pursuit of ever-higher prediction accuracy
with complex models towards practical, deployable solutions that balance
accuracy and parameters such as prediction speed. An MTSC model that has
attracted attention recently is ROCKET, based on random convolutional kernels,
both because of its very fast training process and its state-of-the-art
accuracy. However, the large number of features it utilizes may be detrimental
to inference time. Examining its theoretical background and limitations enables
us to address potential drawbacks and present LightWaveS: a framework for
accurate MTSC, which is fast both during training and inference. Specifically,
utilizing wavelet scattering transformation and distributed feature selection,
we manage to create a solution that employs just 2.5% of the ROCKET features,
while achieving accuracy comparable to recent MTSC models. LightWaveS also
scales well across multiple compute nodes and with the number of input channels
during training. In addition, it can significantly reduce the input size and
provide insight to an MTSC problem by keeping only the most useful channels. We
present three versions of our algorithm and their results on distributed
training time and scalability, accuracy, and inference speedup. We show that we
achieve speedup ranging from 9x to 53x compared to ROCKET during inference on
an edge device, on datasets with comparable accuracy.Comment: This work has been accepted as a short paper at DCOSS 202
Attentive Group Equivariant Convolutional Networks
Although group convolutional networks are able to learn powerful
representations based on symmetry patterns, they lack explicit means to learn
meaningful relationships among them (e.g., relative positions and poses). In
this paper, we present attentive group equivariant convolutions, a
generalization of the group convolution, in which attention is applied during
the course of convolution to accentuate meaningful symmetry combinations and
suppress non-plausible, misleading ones. We indicate that prior work on visual
attention can be described as special cases of our proposed framework and show
empirically that our attentive group equivariant convolutional networks
consistently outperform conventional group convolutional networks on benchmark
image datasets. Simultaneously, we provide interpretability to the learned
concepts through the visualization of equivariant attention maps.Comment: Proceedings of the 37th International Conference on Machine Learning
(ICML), 202
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