45,595 research outputs found
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous
vehicles must make responsible predictions in relation to surrounding
traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and
critical task is to explore the movement patterns of different traffic-agents
and predict their future trajectories accurately to help the autonomous vehicle
make reasonable navigation decision. To solve this problem, we propose a long
short-term memory-based (LSTM-based) realtime traffic prediction algorithm,
TrafficPredict. Our approach uses an instance layer to learn instances'
movements and interactions and has a category layer to learn the similarities
of instances belonging to the same type to refine the prediction. In order to
evaluate its performance, we collected trajectory datasets in a large city
consisting of varying conditions and traffic densities. The dataset includes
many challenging scenarios where vehicles, bicycles, and pedestrians move among
one another. We evaluate the performance of TrafficPredict on our new dataset
and highlight its higher accuracy for trajectory prediction by comparing with
prior prediction methods.Comment: Accepted by AAAI(Oral) 201
Spatial Wireless Channel Prediction under Location Uncertainty
Spatial wireless channel prediction is important for future wireless
networks, and in particular for proactive resource allocation at different
layers of the protocol stack. Various sources of uncertainty must be accounted
for during modeling and to provide robust predictions. We investigate two
channel prediction frameworks, classical Gaussian processes (cGP) and uncertain
Gaussian processes (uGP), and analyze the impact of location uncertainty during
learning/training and prediction/testing, for scenarios where measurements
uncertainty are dominated by large-scale fading. We observe that cGP generally
fails both in terms of learning the channel parameters and in predicting the
channel in the presence of location uncertainties.\textcolor{blue}{{} }In
contrast, uGP explicitly considers the location uncertainty. Using simulated
data, we show that uGP is able to learn and predict the wireless channel
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