3,059 research outputs found
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
In lifelong learning systems, especially those based on artificial neural
networks, one of the biggest obstacles is the severe inability to retain old
knowledge as new information is encountered. This phenomenon is known as
catastrophic forgetting. In this article, we propose a new kind of
connectionist architecture, the Sequential Neural Coding Network, that is
robust to forgetting when learning from streams of data points and, unlike
networks of today, does not learn via the immensely popular back-propagation of
errors. Grounded in the neurocognitive theory of predictive processing, our
model adapts its synapses in a biologically-plausible fashion, while another,
complementary neural system rapidly learns to direct and control this
cortex-like structure by mimicking the task-executive control functionality of
the basal ganglia. In our experiments, we demonstrate that our self-organizing
system experiences significantly less forgetting as compared to standard neural
models and outperforms a wide swath of previously proposed methods even though
it is trained across task datasets in a stream-like fashion. The promising
performance of our complementary system on benchmarks, e.g., SplitMNIST, Split
Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating
mechanisms prominent in real neuronal systems, such as competition, sparse
activation patterns, and iterative input processing, a new possibility for
tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split
mnist/fmnist/not-mnist. Task selection/basal ganglia model has been
integrate
Systematic Serendipity: A Test of Unsupervised Machine Learning as a Method for Anomaly Detection
Advances in astronomy are often driven by serendipitous discoveries. As
survey astronomy continues to grow, the size and complexity of astronomical
databases will increase, and the ability of astronomers to manually scour data
and make such discoveries decreases. In this work, we introduce a machine
learning-based method to identify anomalies in large datasets to facilitate
such discoveries, and apply this method to long cadence lightcurves from NASA's
Kepler Mission. Our method clusters data based on density, identifying
anomalies as data that lie outside of dense regions. This work serves as a
proof-of-concept case study and we test our method on four quarters of the
Kepler long cadence lightcurves. We use Kepler's most notorious anomaly,
Boyajian's Star (KIC 8462852), as a rare `ground truth' for testing outlier
identification to verify that objects of genuine scientific interest are
included among the identified anomalies. We evaluate the method's ability to
identify known anomalies by identifying unusual behavior in Boyajian's Star, we
report the full list of identified anomalies for these quarters, and present a
sample subset of identified outliers that includes unusual phenomena, objects
that are rare in the Kepler field, and data artifacts. By identifying <4% of
each quarter as outlying data, we demonstrate that this anomaly detection
method can create a more targeted approach in searching for rare and novel
phenomena.Comment: 21 pages, 10 figures, Submitted to MNRA
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Co-creating & Implementing a Reasonable Adjustments Framework in an acute hospital trust
This poster offers evidence of practice development activities undertaken to establish a clinical framework for making reasonable adjustments in acute hospital and application of the tool in a mortality audit.Peer reviewe
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