2,820 research outputs found
Pulling Out All the Tops with Computer Vision and Deep Learning
We apply computer vision with deep learning -- in the form of a convolutional
neural network (CNN) -- to build a highly effective boosted top tagger.
Previous work (the "DeepTop" tagger of Kasieczka et al) has shown that a
CNN-based top tagger can achieve comparable performance to state-of-the-art
conventional top taggers based on high-level inputs. Here, we introduce a
number of improvements to the DeepTop tagger, including architecture, training,
image preprocessing, sample size and color pixels. Our final CNN top tagger
outperforms BDTs based on high-level inputs by a factor of --3 or more
in background rejection, over a wide range of tagging efficiencies and fiducial
jet selections. As reference points, we achieve a QCD background rejection
factor of 500 (60) at 50\% top tagging efficiency for fully-merged (non-merged)
top jets with in the 800--900 GeV (350--450 GeV) range. Our CNN can also
be straightforwardly extended to the classification of other types of jets, and
the lessons learned here may be useful to others designing their own deep NNs
for LHC applications.Comment: 33 pages, 11 figure
Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas
Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's
retention time (RT) may not stay fixed across multiple chromatograms. To use
GC-MS data for biomarker discovery requires alignment of identical analyte's RT
from different samples. Current methods of alignment are all based on a set of
formal, mathematical rules. We present a solution to GC-MS alignment using deep
learning neural networks, which are more adept at complex, fuzzy data sets. We
tested our model on several GC-MS data sets of various complexities and
analysed the alignment results quantitatively. We show the model has very good
performance (AUC for simple data sets and AUC for very
complex data sets). Further, our model easily outperforms existing algorithms
on complex data sets. Compared with existing methods, ChromAlignNet is very
easy to use as it requires no user input of reference chromatograms and
parameters. This method can easily be adapted to other similar data such as
those from liquid chromatography. The source code is written in Python and
available online
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
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